The tree classifier is an effective method in statistical pattern classification. In this paper, a new tree-classifier design approach based on a splitting algorithm is proposed. This method not only has the common advantages of the tree classifier, but also enables its user to make a trade-off between accuracy and economy of computing time, and to achieve satisfactory results with a relatively short computing time. Experimental classification for test data and remote-sensing data is discussed.