In this paper, we consider signals originated from a sequence of sources. More specifically, the problems of segmenting such signals and relating the segments to their sources are addressed. This issue has wide applications in many fields. This report describes a resolution method using an Ergodic Hidden Markov Model (HMM), in which each HMM state corresponds to a signal source. The signal source sequence can be determined by using a decoding procedure (Viterbi algorithm or Forward algorithm) over the observed sequence. Baum-Welch training is used to estimate a HMM parameters from training material. As an application of a multiple signal source identification problem, an experiment is performed on unknown speaker identification. Result show a classification rate of 79% for 4 male speakers. The results also indicate that the model is sensitive to the initial values of the Ergodic HMM and that employing the long-distance LPC cepstrum is effective for signal preprocessing.