In this paper we describe an approach for automatic recognition of tennis strokes using a single low cost camera. Professional tennis is played at high speed so the ability to classify tennis strokes on camera is hindered by the rapid movement of the players. We have developed an accurate recognition system which can automatically index tennis strokes from video footage. We aim to evolve this system so that meta data, such as time codes and descriptions of the strokes played, can be automatically indexed for a training session or a match. This level of indexing would provide an excellent foundation for the development of next generation sports coaching systems. The aim of this paper is to coarsely classify the main stokes played in tennis, i.e. a serve, forehand or backhand. The proposed approach is evaluated against a real-world dataset, obtained from elite players in a competitive training match.