Pattern recognition methods are described for classifying acoustic emission (AE) signals according to their source types. Simple time and frequency domain features of the AE waveforms are used in the classification to distinguish one type from another. Methods for classification using labeled waveforms, and clustering using unlabeled waveforms have been developed and applied to the detection of a fatigue crack growing from a fastener hole in a simulated aircraft structure. Sources of AE in this monitoring application are crack growth, crack face rubbing, fastener fretting, mechanical impacts, electrical transients, and hydraulic noise. Classification of labeled data to separate crack-related AE from the other types produced a 96-100% accuracy, and clustering of unlabeled data pro-duced an 82-94% accuracy. A system calibration method needs to be developed before the pattern recognition algorithms can reliably accommodate specimen geometry changes.