The views expressed in this thesis are those of the author and do not reflect the official policy or position of the Department of Defense or the U.S. Government. / The main objective of this study is to find a wavelet-based, feature extracting algorithm for push-to-talk transmitter identification. A distance-measure algorithm is introduced to classify signals belonging to one of four transmitters. The signals are first preprocessed to put them into a form suitable for wavelet analysis. The preprocessing scheme includes taking the envelopes and differentials. Median filtering is also applied to the outputs of the wavelet transform. The distance algorithm uses local extrema of the wavelet coefficients, and computes the distance between the local extrema of a template and the processed signals. A small distance implies high similarity . A signal from each transmitter is selected as a template. A small distance measure indicates that the signal belongs to the transmitter from which the template originated. The distance algorithm can classify correctly the four different signal sets provided for the research. Even at lower signal-to-noise levels, good identification is achieved.