In this paper we propose an approach to high-level classification of video into genre: sport, cartoon, news, commercial and music. An important issue for automatic high-level classification systems is the amount of time needed to classify a video. Here we investigate classification performance as a function of the test sequence length. In addition we present performance against different orders and combinations of static and dynamic mel-frequency cepstral coefficients (MFCC). We find that static and delta MFCCs perform well for this classification task. A test sequence length of approximately 25 seconds for the 5 class problem gives approximately 80% correct identification.