Many studies have tried to optimize parameters of case-based reasoning (CBR) systems. Among them, selection of appropriate features to measure similarity between the input and stored cases more precisely, and selection of appropriate instances to eliminate noises which distort prediction have been popular. However, these approaches have been applied independently although their simultaneous optimization may improve the prediction performance synergetically. This study proposes a case-based reasoning system with the two-dimensional reduction technique. In this study, vertical and horizontal dimensions of the research data are reduced through our research model, the hybrid feature and instance selection process using genetic algorithms. We apply the proposed model to a case involving real-world customer classification which predicts customers' buying behavior for a specific product using their demographic characteristics. Experimental results show that the proposed technique may improve the classification accuracy and outperform various optimized models of the typical CBR system. (C) 2006 Elsevier Ltd. All rights reserved.