The problem studied in this paper, is the design of an electromagnetic target classifier by using a natural resonance based electromagnetic feature extraction technique applied to the small-scale aircraft targets. The aircraft targets are modeled by perfectly conducting, thin wire structures and the electromagnetic fields back-scattered from targets are numerically generated for five aircraft models. The Wigner-Ville time-frequency distribution (WD) is applied to the electromagnetic back-scattered responses of targets from different aspects. Then, feature vectors are extracted from suitably chosen late-time portions of the WD outputs, which include natural resonance related information for every target and aspect to decrease aspect dependency. The database of the classifier is constructed by the feature vectors extracted at only a few reference aspects. Principal components analysis is also used to fuse the feature vectors and/or late-time aircraft responses extracted from reference aspects of a given target into a single characteristic feature vector of that target to further reduce aspect dependency. Consequently, an almost aspect independent classifier is designed for small-scale aircraft targets reaching high correct classification rate. © 2005 IEEE.