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Automatic recognition of musical patterns plays a crucial part in Musicological and Ethno-musicological research and can become an indispensable tool for search and comparison of music extracts within a large multimedia database. This paper suggests an efficient method for recognizing isolated musical patterns in a monophonic environment. Each pattern to be recognized is converted into a sequence of frequency jumps by means of a "fundamental frequency tracking" algorithm, followed by a quantizer. The resulting sequence of frequency jumps is presented to the input of the recognizer which uses a combination of Hidden Markov Model and Dynamic Time Warping. The main characteristic of Hidden Markov Model is that it utilizes the stochastic information from the musical frame to recognize the pattern. The methodology is tested in the context of South Indian classical music, which exhibits certain characteristics that make the classification task harder when compared to Western music. Perfect recognition has been obtained for all the musical clips in nine typical music patterns used in practice. South Indian classical instrument, flute, is used for the whole experiment.