A study of the effectiveness of low-frequency electromagnetic responses for identifying objects of complex shape is presented. The linear separability of a large variety of objects such as cubes, cylinders and aircraft were examined. Two classification algorithms, a linear discriminant and a nearest neighbour rule, were used to classify a set of four aircraft models. The classification performance is presented in terms of the probability of misclassification versus noise level. The effectiveness of the various combinations of electromagnetic features was evaluated. The results indicate that amplitude, phase and polarization all contribute substantial amounts of target information. Making the assumption of aprioriknowledge of the target's approximate aspect angle, a reliable classification can be attained utilizing a rather small number of frequencies.