Feature extraction and fusion of target signatures is of great importance in the detection, tracking, and identification of tracked and wheeled vehicles. Current research is centered in developing a set of robust features which will allow classification with high confidence regardless of various environmental conditions. The performance of boundary decision and nonparametric classifier topologies is also of special interest for the acoustic/seismic feature space. Acoustic and seismic data collected using co-located sensors have been processed using harmonic line association [J. A. Robertson, IIT Research Institute, in-house report] and higher order statistic [F. B. Shin and D. H. Kil, Proceedings of the International Conference on Image Processing, 3 (1995)] feature extraction techniques for classification. Performance results of the feature fusion will be presented as well as several classifier architectures. Real-time performance will also be presented [N. Srour, Remote Netted Acoustic Detection System, Army Res. Lab., May (1995)].