We have developed practical strategies for discriminating between buried unexploded ordnance (UXO) and metallic clutter. These methods are applicable to time-domain electromagnetic data acquired with multistatic, multicomponent sensors designed for UXO classification. Each detected target is characterized by dipole polarizabilities estimated via inversion of the observed sensor data. The polarizabilities are intrinsic target features and so are used to distinguish between UXO and clutter. We tested this processing with four data sets from recent field demonstrations, with each data set characterized by metrics of data and model quality. We then developed techniques for building a representative training data set and determined how the variable quality of estimated features affects overall classification performance. Finally, we devised a technique to optimize classification performance by adapting features during target prioritization.