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Cardiac arrhythmia analysis is one important biomedical application of pattern recognition. In this paper we present a new pattern recognition technique applied to the analysis of electrograms during atrial fibrillation. Atrial fibrillation (AF) is a common arrhythmia which has a high rate of incidence among the elderly. Besides being poorly tolerated, it greatly increases the risk of embolic stroke. In this paper we propose a novel algorithm based on independent component analysis for classifying multichannel electrograms from an ovine model of AF into one of four classes - normal sinus rhythm, atrial flutter, paroxysmal AF and chronic AF. The success rates achieved indicate great potential of the method in automated electrogram analysis and classification.