This paper presents an efficient method for recognizing isolated musical patterns in a monophonic environment, using a novel extension of Dynamic Time Warping, which we call Context Dependent Dynamic Time Warping. Each pattern 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 employs Context Dependent Dynamic Time Warping. The main characteristic of Context Dependent Dynamic Time Warping is that it exploits the correlation exhibited among adjacent frequency jumps of the feature sequence. The methodology has been tested in the context of Greek Traditional Music, which exhibits certain characteristics that make the classification task harder, when compared with Western musical tradition. A recognition rate higher than 95% was achieved.