An application of artificial neural networks (ANN) for discrimination between natural earthquakes and underground nuclear explosions has been studied using distance corrected spectral data of regional seismic phases. Pn, Pg, and Lg spectra have been analyzed from 83 western U.S. earthquakes and 87 Nevada Test Site explosions recorded at the four broadband seismic stations operated by Lawrence Livermore National Laboratory. Distance corrections are applied to the raw spectra using existing frequency-dependent Q models for the Basin and Range. The spectra are sampled logarithmically at 41 points between 0.1 and 10 Hz for each phase and checked for adequate signal-to-noise ratios (S/N > 2). The ANN was implemented on a SUN 4/110 workstation using a backpropagation-feedforward architecture. We find that, using even simple ANN architectures (82 input units, 1 hidden unit, and 2 output units), powerful discrimination systems can be designed. In order to regionalize the data characteristics, a separate neural network was assigned to each station. For this data set, the rate of correct recognition for untrained data is over 93 per cent for both earthquakes and explosions at any single station. Using a majority voting scheme with a network of four stations, the rate of correct recognition is over 97 per cent. Although the performance of the ANN is similar to that of the Fisher linear discriminant, the ANN exhibits a number of computational advantages over the conventional method. Finally, examination of the network weights suggests that, in addition to spectral shape, a criterion that the ANN utilized to discriminate between the two populations was the Lg/Pg spectral amplitude ratios.