This paper addresses the problem of automatic speech recognition in the presence of interfering signals and noise with statistical characteristics ranging from stationary to fast changing and impulsive. A technique of signal decomposition using hidden Markov models, [lj, is described. This is a generalisation of conventional hidden Markov modelling that provides an optimal method of decomposing simultaneous processes. The technique exploits the ability of hidden Markov models to model dynamically varying signals in order to accomodate concurrent processes, including interfering signals as complex as speech. This form of signal decomposition has wide implications for signal separation in general and improved speech modelling in particular. However. this paper concentrates on the application of decomposition to the problem of recognition of speech contaminated with noise. 1