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The automatic classification of environmental noise sources from their acoustic signatures recorded at the microphone of a noise monitoring system (NMS) is an active subject of research nowadays. This paper shows how hidden Markov models (HMM's) can be used to build an environmental noise recognition system based on a time-frequency analysis of the noise signal. The performance of the proposed HMM-based approach is evaluated experimentally for the classification of five types of noise events (car, truck, plane, train, dog). We propose several techniques of features extraction in order to perform the recognition. Various design issues such as features definition and extraction, classification algorithms and performance evaluation methods are explored. The major part of this paper is dedicated to the discussion of our classification results using various features and classification techniques.