More than 2500 ambient noise samples were collected in Behm Canal, Alaska, between July 1989 and January 1990. The data were obtained using a system deployed from a MINIMET buoy, and consist of one‐third octave analyses covering the 50 Hz through 63 kHz bands. Signatures obtained on an hourly schedule, were transmitted via the ARGOS satellite and entered into a LOTUS 1‐2‐3 database. Attempts to categorize the data by conventional means, e.g., manual sorting of plots of the signatures, or on a purely statistical basis, were unsatisfactory: thus the application of artificial neural networks was investigated. This approach yielded reasonable classifications and allowed atypical signatures to be identified and deleted from the database prior to performing statistical analysis. The groupings assigned by the network parallel those which might have been made by an analyst in that obviously different signatures are assigned to different classes, but in some cases the network makes more subtle distinctions than an analyst might. One network separated the signatures into 22 major groups (those containing at least 26 signatures) that include 90% of the data.