||The prediction of bank performance is an important issue. The bad performance of banks may first result in bankruptcy, which is expected to influence the economics of the country eventually. Since the early 1970s, many researchers had already made predictions on such issues. However, until recent years, most of them have used traditional statistics to build the prediction model. Because of the vigorous development of data mining techniques, many researchers have begun to apply those techniques to various fields, including performance prediction systems. However, data mining techniques have the problem of parameter settings. Therefore, this study applies particle swarm optimization (PSO) to obtain suitable parameter settings for support vector machine (SVM) and decision tree (DT), and to select a subset of beneficial features, without reducing the classification accuracy rate. In order to evaluate the proposed approaches, dataset collected from Taiwanese commercial banks are used as source data. The experimental results showed that the proposed approaches could obtain a better parameter setting, reduce unnecessary features, and improve the accuracy of classification significantly.