When we use Case-Based Reasoning (CBR) for practical applications, it is often the case to implement two kinds of problem solvers: casebased one and knowledge-based/conventional one. The complex integration of such plural reasoners often causes serious VV~T problems: performance of problem solving, quality of solutions, and so on. In this paper, we ~ldress the performance validation problems of a CBR system. This paper presents a blackbox validation approach to quantitatively analyze the performance and proposes three kinds of performance measures. The proposed measures are then applied to validating a CBR system: IRS-CHR (Intelligent Information Retriever with a Case-Based Reasoner). From the experimental results, we conclude that (1) IRS-CBR has succeeded in both speed-up and memory-based learning, and (2) the proposed measures are useful for validating a CBR system.