The National Toxicology Program (NTP) uses the results of various experiments to determine if test agents are carcinogenic. Because these experiments are costly and time consuming, the rate at which test agents can be tested is limited. The ability to predict the outcome of the analysis at various points in the process would facilitate informed decisions about the allocation of testing resources. In addition, it is hoped that models resulting from attempting to make such predictions will augment expert insight into the biological pathways associated with cancers. This paper describes an approach to making such predictions which is based on learning Bayesian Classifiers. This method builds predictive models by gleaning information from a training set containing data from test agents which have previously been classified by NTP. We will focus on the structure of the training sets and the intuitiveness and cross-validated accuracy of the models learned. As the data is available, these models are being used to predict the classifications of a set of thirty test agents currently being bioassayed by NTP.