In Hindi, affirmative particle "hã" carries out a variety of discourse functions. Preliminary investigation has shown that though it is difficult to disambiguate these different functions from prosody alone, there seems to be a distinct prosodic pattern associated with each of these. In this paper, we present a corpus study of spoken utterances of the Hindi word "hã". We identify these prosodic patterns and capture the specific pitch variations associated with each of the various functions. We also examine the use of prosodic cues in classification of the utterances into different functions using k-means clustering. While certain amount of speaker dependency, as well as lack of contextual and lexical information resulted in high classification entropy, however, the results were consistent with comparable studies in other languages.