The temporal and spectral features of the acoustic signatures of aircraft and highway vehicles are used to train two types of neural networks. The detection and classification performances of the networks are then evaluated using an independent set of data. The types of networks evaluated are (i) feedforward with backpropagation training, and (ii) probabilistic nets with maximum-likelihood training. The results demonstrate accurate classification as to type of vehicle during aircraft takeoff or under load conditions for heavy trucks, depending on the signal-to-noise ratio. Neural nets appear to offer a promising vehicle classifier for monitoring airport and highway traffic for compliance with noise regulations.