|Abstract||A central problem in music information retrieval is finding suitable representations which enable efficient and accurate computation of musical similarity and identity. Low level audio features are ideal for calculating identity, but are of limited use for similarity measures, as many aspects of music can only be captured by considering high level features. We present a new method of characterising music by typical bar-length rhythmic patterns which are automatically extracted from the audio signal, and demonstrate the usefulness of this representation by its application in a genre classification task. Recent work has shown the importance of tempo and periodicity features for genre recognition, and we extend this research by employing the extracted temporal patterns as features. Standard classification algorithms are utilised to discriminate 8 classes of Standard and Latin ballroom dance music (698 pieces). Although pattern extraction is error-prone, and patterns are not always unique to a genre, classification by rhythmic pattern alone achieves up to 50% correctness (baseline 16%), and by combining with other features, a classification rate of 96% is obtained.