Methods and systems for recognition of concurrent, superimposed, or otherwise overlapping signals are described. A Markov Selection Model is introduced that, together with probabilistic decomposition methods, enable recognition of simultaneously emitted signals from various sources. For example, a signal mixture may include overlapping speech from different persons. In some instances, recognition may be performed without the need to separate signals or sources. As such, some of the techniques described herein may be useful in automatic transcription, noise reduction, teaching, electronic games, audio search and retrieval, medical and scientific applications, etc.