The electromagnetic target classification is a challenging problem since the scattered field from a target is highly dependent on operating frequency, polarization and aspect angle. In order to minimize adverse effects of these dependencies an intelligent classifier containing some distinguishable target features is needed. In addition, in order to be suitable for real target applications, the properties of operating with moderate frequency bandwidth and discriminating an alien target from a target set containing friend targets are important. In this study, an electromagnetic target classification method for isolated targets using noisy data in the classifier design to obtain high accuracy performance in a wider SNR range and having the ability of discrimination of an alien target without any priori information is introduced. The proposed method is mainly based on a late-time resonance region target classification technique, which was reported recently to use the multiple signal classification (MUSIC) algorithm and natural-resonance mechanism modeled by singularity expansion method (SEM) for target feature extraction, and modified for target sets containing alien target(s). The proposed classifier design method is demonstrated and tested for a target set of five friend and one alien small-scale aircraft targets. According to the test result, the proposed method gives high accuracy rates for this target set.