We consider the problem of classifying an unknown observation into one of several populations using tree-structured allocation rules. Although many parametric classification procedures are robust to certain assumption violations, there is need for discriminant procedures that can be utilized regardless of the group-conditional distributions that underline the model. The tree-structured allocation rule will be discussed. Finally, Monte Carlo results are reported to observe the performance of the rule in comparison to a discriminant and logistic regression analysis.