Automatic recognition of musical patterns plays a crucial part in Musicological and Ethno musicological research and can become an indispensable tool for the search and comparison of music extracts within a large multimedia database. This paper finds an efficient method for recognizing isolated musical patterns in a monophonic environment, using Hidden Markov Model. 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 use Hidden Markov Model. 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 with Western musical tradition. Recognition of 100% has been obtained for the six typical music pattern used in practise. South Indian classical instrument, flute is used for the whole experiment.