This paper presents a robust classification of dialog acts from text utterances. Two different types, namely, bag-of-words and syntactic relationship among words, were used to extract the discourse level features from the transcript of utterances. Subsequently a number of feature mining methods have been used to identify the most relevant features and their roles in classifying dialog acts. The selected features are used to learn the underlying models of dialog acts using a number of existing machine learning algorithms from the WEKA toolbox. Empirical analyses using the HCRC Map Task Corpus dialog data was conducted to evaluate the performance of the proposed approach.