Knowledge discovery approach was made in the domain of pipe organ sounds. The background of such an approach was reviewed and some experimental results were quoted. Parameterization of pipe organ sounds was discussed, and a feature extraction method was described in terms of parameters derived from both time-and frequency-domain analyses. The knowledge acquisition system principles were described and the neural network algorithms underlying both training and performance processes were presented. The number of necessary data inputs and the structure of neural net were studied in order to obtain the demanded accuracy of results. Experimental results have shown that the technique applied to the analysis of musical events in pipe organ sound might form an approach to the domain of automatic recognition of events in sound.