Unattended ground sensor (UGS) networks are intended to detect and localize the presence of strategic relocatable targets in the theater of operation over several kilometers. Passive acoustic sensors, an integral part of UGS, have achieved a high level of maturity and will allow acoustic target classification for tracked and wheeled vehicles. Of primary importance in the classification problem is the selection of a robust feature extraction technique, tolerant of both the environment and the nonstationary nature of the acoustic signatures. Several feature extraction techniques were used with experimental acoustic data collected from a small baseline, circular array. Results will be presented of the classification for acoustic features using a backpropagation neural network with simple power spectrum, harmonic line association [J. A. Robertson, IIT Research Institute, in-house report], principal components [J. Mao and A. K. Jain, IEEE Trans. Neural Networks 6 (2) (1995)], and wavelet packet [K. Etemad and R. Chellappa, Proc. First Intl. Conf. on Image Processing (November 1994)] feature extraction techniques.