Automatic Target Recognition (ATR) algorithm performance is sensitive to variability in the observed target signature. Algorithms are developed and tested under a specific set of operating conditions and then are often required to perform well under very different conditions (referred to as Extended Operating Conditions, or EOCs). The stability of the target signature as the operating conditions change dictates the success or failure of the recognition algorithm. Laser vibrometry is a promising sensor modality for vehicle identification because target signatures tend to remain stable under a variety of EOCs. A micro-doppler vibrometry sensor measures surface deflection at a very high frequency, thus enabling the surface vibrations of a vehicle to be sensed from afar. Vehicle identification is possible since most vehicles with running engines have a unique vibration signature defined by the engine type. In this paper, we present an ATR algorithm that operates over data collected from a set of accelerometers. These contact accelerometers were placed at a variety of locations on three target vehicles to emulate an ideal laser vibrometer. We discuss a set of features that are useful for discrimination of the three different target categories. We also present classification results based on these features.