The decision tree method for classification problems has been extended to accommodate multiple dependent properties. When applied to drug discovery efforts this means a separate activity class can be predicted for each of several targets with a single tree model. A new tree representation and growth procedure, PUMP-RP, has been developed. The final architecture of the tree allows for easy interpretation as to which independent variables and split values are important for all targets and which are specific to a given target. It should thus be usefully applied to studies of drug specificity. A side benefit of the new method is that it can make use of data with missing (or even sparse) dependent property values. This has the potential to leverage copious data from an older, well-studied target while beginning to study a newer target for which only a small amount of data are available.