In the computer interpretation of seismic data, the critical first step is to identify the general class of an unknown event. For example, the classification might be: teleseismic, regional, local, vehicular, or noise. We have studied self organizing neural networks (SONNs) for classifying such events. Both Kohonen and Adaptive Resonance Theory (ART) SONNs were developed and tested with a moderately large set of real seismic events. Given the detection of a seismic event and the corresponding signal, we compute the time-frequency distribution, its binary representation, and finally a shift-invariant representation, which is the magnitude of the two-dimensional Fourier transform (2-D FFT) of the binary time-frequency distribution. This preprocessed input is fed into the SONNs. The overall results based on 111 events (43 training and 68 test events) show that SONNs are able to group events that `look' similar. The Kohonen algorithm has the advantage of being able to develop a higher resolution set of clusters. We also find that the ART algorithm has the advantage of being able to develop a higher resolution set of clusters. We also find that the ART algorithm has an advantage in that the number of cluster groups do not need to be predefined. When a net type of event is detected, the ART network is able to handle the event rather gracefully. A modified Kohonen algorithm is developed to exploit each algorithm. The results from the SONNs together with an expert seismologist's classification are then used to derive fuzzy event classifications. The integration of the SONN into the Network Seismic Event Analyzer (NetSEA) expert system is described and a strategy to integrate the fuzzy classifications into the inference engine is also proposed.