The statistical classification of "N" individuals into "G" mutually exclusive groups when the actual group membership is unknown is common in the social and behavioral sciences. The results of such classification methods often have important consequences. Among the most common methods of statistical classification are linear discriminant analysis, quadratic discriminant analysis, and logistic regression. However, recent developments in the statistics literature have brought new and potentially more flexible classification models to the forefront. Although these new models are increasingly being used in the physical sciences and marketing research, they are still relatively little used in the social and behavioral sciences. The purpose of this article is to provide a comparison of these modern methods with the classical methods widely used in situations that are relevant in the social and behavioral sciences. This study uses a large-scale Monte Carlo simulation study for the comparisons, as analytic comparisons are often not tractable. Results indicate that classification and regression trees generally produced the highest classification accuracy of all techniques tested, though study design characteristics such as sample size and model complexity can greatly influence optimal choice or effectiveness of statistical classification method. (Contains 7 tables and 3 figures.)