This paper utilizes four different classification procedures (binary choice models) and compares their ability to predict corporate takeovers. Specifically, the paper develops logit, probit, discriminant, and recursive partitioning, models to predict which firms will be taken over. The original classification accuracy and the validation test results indicate that the recursive partitioning model outperforms the other models (although its accuracy drops significantly in the validation test). The results also indicate the difficulty in predicting corporate takeovers.