Case-based reasoning (CBR) is a paradigm of artificial intelligence and cognitive science that models the reasoning process as primarily memory based. Case-based reasoners solve new problems by retrieving stored ‘cases’ describing similar prior problem-solving episodes and adapting their solutions to fit new needs. CBR research studies the CBR process both as a model of human cognition and as an approach to building intelligent systems. Principles from CBR research serve as a foundation for applied computer systems for tasks such as supporting human decision-making, aiding human learning, and facilitating access to electronic information repositories.The description of a new problem to be solved is introduced in the problem space. During the first step, retrieval, a new problem is matched against problems of the previous cases by computing similarity function, and the most similar problem and its stored solution are found. If the proposed solution does not meet the necessary requirements of a new problem situation, the next step, adaptation, occurs and a new solution is created. A received solution and a new problem together form a new case that is incorporated in the case base during the learning step. In this way CBR system evolves into a better reasoner as the capability of the system is improved by extending of stored experience.