Knowledge Management Performance measurement Knowledge management (KM) is one of the emerging topics of academic and professional discourse in many fields of knowledge, including cognitive sciences, sociology, management science, computer science, information science , knowledge engineering, artificial intelligence, and economics ([Dalkir, 2005], [Martin, 2008], [Sinotte, 2004], [Rowley, 2007] .The importance of managing knowledge is also getting more and more attention in all types of organizations, including businesses, government bodies, research institutes, Non-Governmental Organizations (NGOs), and international development and financial institutions ([Blair, 2002] and [Chua, 2009]), since beginning of nineties, it is recognized as a significant factor in gaining a competitive advantage in our competitive business environment [Nonaka and Takeuchi, 1995]. Evidently, there is a tendency by the different professions interested in KM, as well as different researcher to present and interpret what constitutes KM from their own perspective and their own profession ([Dalkir, 2005], [Ekbia and Hara, 2007], [Hlupic et al., 2002], [Jashapara, 2005], [Liao et al., 2004], [McInerney, 2002], [Sarrafzadeh et al., 2006] and [Widén-Wulff et al., 2005]). Consequently, developments in KM are influenced by the different professions interested in knowledge ([Dalkir, 2005], [Jashapara, 2005], [Martin, 2008], [Rowley, 2007], [Sarrafzadeh et al., 2006] and [Sinotte, 2004]). This has resulted in different theories as well as different models, definitions, terminology but among them and commonly, KM can be defined as a multi-disciplined approach for achieving organizational objectives by making the best use of knowledge which is divided into tacit and explicit knowledge. This knowledge may be viewed in term of culture , structure and technology and that’s compose the Three pillar of KM which were named by Minonne as : Organizational learning management OLM , Organizational knowledge Management OKM and Intellectual capital management ICM [Minonne and Turner , 2010]. Maintaining these pillars is a powerful means to improve the level of the whole performance of an organization. Thus, companies should invest in knowledge management projects in order to amplify the creation, the sharing and the transfer of explicit and tacit knowledge. Several techniques can be considered to develop and implement knowledge management and organizational memory systems, according to the type of organization, its needs, and its culture as well as the nature of knowledge to be capitalized. From this need , emerge several topics and object of research that should contribute to this field development ,among them we can list : Knowledge Management Systems &Applications , knowledge-based approaches, Knowledge Representation and Ontology , Knowledge Sharing and Exchange ,Knowledge Acquisition and Evolution, Uncertainty and Vagueness in Knowledge Modeling , Knowledge Integration ,Agent-based Approaches to Knowledge Management , KM for Collaboration and Decision Support ,Knowledge Management Maturity Models , Case-based Reasoning for KM (CBR) , Collective and Collaborative Intelligence ,Knowledge Management and Social Computing , KM in Web2.0 and the Semantic Web , Knowledge Visualization , Knowledge and Business Process Modeling , Evaluation and Measurement of Required Knowledge , KM in Collaborative Software Development One of the most challenging issues on the field of KM is that of measurement, « that which doesn’t get measured, doesn’t get managed » [Redman, 1998], Organizations success are becoming increasingly dependent on knowledge that its strategic success rely on the effective management of its knowledge assets, for this an effective way of assessing performance is a must , [Turner and JacksonCox,2002], and Metrics that measure the performance of KM integration are needed , to justify KM initiatives , budgets as well as guiding future strategic firms . On establishing these set of KPI and metrics , should resist to the temptation to focus on what is measurable « Tangibles assets » to outcomes that meet really organizational need, such as intellectual capital ,which are more likely to represent KM effectiveness dimension than efficiency [Sveiby , 2010 , intangibles assets] A common starting point of each measurement method is the categorization of different forms of KM integration and especially for intangibles assets which represent a high difficulty to measure. One of the most cited model for KM integration, identify four complementary integration forms, these are: Cultural Integration, Organizational integration, Methodical integration and procedural integration [Turner and Minonne 2009] Then coming methods to define pertinent targets as well as setting appropriate quantitative or qualitative key performances indicators, this task have the difficulty to come with appropriate targets according to strategy, and choose between reductionistic/additive measures to combinatorial ones for definition of the whole KM index performance. Over the last thirty years , many framework and measuring system were introduced , from Balance scoreboard system by Kaplan and Norton (1992), the most widely system taking account of non financial data , to knowledge management Monitor KM2 framework , last years are seeing a new breed of measurement system on which intellectual capital is center stage . On our Work, we will try to make the inventory of main relevant measurement methods and systems developed for evaluating KM performance, our study will focus on the efficiency of each method with a comparative and critical sight Having the novelty and emergency of this field, We aim beyond this paper , to constitute a reference paper that incorporate all works regarding this subject, that provide to scholar , practitioner and researcher a global view of state and progress of the measurement of Knowledge management performance , with the added value of concision , and improvement axis for each of studied system. References: Dalkir, 2005 K. Dalkir, Knowledge management in theory and practice, Elsevier Butterworth-Heinemann, Burlington (2005). Martin, 2008 B. Martin, Knowledge management, C. Blaise, Editor, Annual review of information science and technology (ARIST), vol. 42, Information Today, Inc., Medford, NJ (2008), pp. 371–424.v Sinotte, 2004 M. 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