This study investigates the attribute selection problem for reducing the number of software metrics (program attributes) used by a case-based reasoning (CBR) software quality classification model. The metrics are selected using the Kolmogorov-Smirnov (K-S) two sample test. The "Modified Expected Cost of Misclassification" measure, recently proposed by our research team, is used as a performance measure to select, evaluate, and compare classification models. The attribute selection procedure presented in this paper can assist a software development organization in determining the software metrics that are better indicators of software quality. By reducing the number of software metrics to be collected during the development process, the metrics data collection task can be simplified. Moreover, reducing the number of metrics would result in reducing the computation time of a cbr model. Using an empirical case study of a real-world software system, it is shown that with a reduced number of metrics the cbr technique is capable of yielding useful software quality classification models. Moreover, their performances were better than or similar to cbr models calibrated without attribute selection.