Appropriate placement into courses at postsecondary institutions is critical for the success of students in terms of retention and graduation rates. To reduce the number of students who are misplaced, using multiple measures in placing students is encouraged. However, in practice most postsecondary schools utilize only a few measures to determine course placement. One of the reasons is the lack of research on methodologies that can be used to develop and establish appropriate cutoff scores using multiple measures. The purpose of this study is to investigate whether the classification tree model is a useful alternative approach to the multiple logistic regression model for placement into college courses. For comparison, this research examined the approaches' effectiveness and predictive accuracy for College Algebra. Data were obtained from two medium-sized four year, midwestern institutions. Using two well-known tree models, important measures and their cutoff scores for College Algebra were determined. The findings of this study showed that tree models were an effective alternative approach. Tree models also performed better than, or as well as, the multiple logistic regression model in predictive accuracy.