In a study at a military range with the objective to discriminate potentially hazardous mortars from nonhazardous shrapnel, range, and cultural debris, six different discrimination techniques were tested using data from an array of magnetometers, a time-domain electromagnetic induction (EMI) cart, an array of time-domain sensors, and a time-domain EMI cart with a wider measurement bandwidth. Discrimination was achieved using rule-based or statistical classification of feature vectors extracted from dipole or polarization tensor models fit to detected anomalies. For magnetics, the ranking by moment yielded better discrimination results than that of apparent remanence from relatively large remanent magnetizations of several of the seeded items. The magnetometer results produced very accurate depths and fewer failed fits attributable to noisy data or model insuffi-ciency. The EMI-based methods were more effective than the magnetometer for intrinsic discrimination ability. The higher signal-to-noise ratio, denser coverage, and more precise positioning of the EM-array data resulted in fewer false positives than the EMI cart. When depth constraints from the magnetometer data were used to constrain the EMI fits through cooperative inversion, discrimination performance improved considerably. The wide-band EMI sensor was deployed in a cued-interrogation mode over a subset of anomalies. This produced the highest-quality data because of collecting the densest data around each target and the additional late time-decay information available with the wide-band sensor. When the depth from the magnetometer was used as a constraint in the cooperative inversion process, all mortars were recovered before any false positives were encountered.