A procedure for predicting categorical outcomes using categorical predictor variables was described by Moonan. This paper describes a related technique which uses prior probabilities, updated by joint likelihoods, as classification criteria. The procedure differs from Moonan's in that the outcome having the greatest posterior probability is selected as the prediction regardless of misclassification cost. It also differs in method of screening and weighting the predictor variables, and treats the problem of small-sample bias. Applications, to date, are in the analysis and use of questionnaire responses to predict categorical outcomes, namely, voluntary, academic, and military attrition from a Service College. Classification efficiency appears to be comparable to that of the Moonan technique. (Author)