In this work, we build a system that uses a decision tree to predict fungal protein localization based on physiochemical properties of proteins calculable from their primary sequences. The training examples that serve as basis for learning are obtained from experimentally validated localizations. Although there is clear evidence of presence of the same protein in more than one sub-cellular compartment, almost all existing automated systems restrict their predictions to single-site localization. Here, we attempt to address this issue and for proteins that are reported to target more than one sub-cellular location, our system predicts as many localization sites as possible.  When localizing among 17 sub-cellular compartments, in 64% of the cases our system successfully predicts at least one of the experimentally reported localizations. In addition, our results indicate that all the reported localizations are correctly predicted in 49% of the cases. We also report 76 fungal protein features implicated in localization and indicate those with the highest relative discriminatory power. Finally, we report on necessary conditions for localization to specific sub-cellular sites