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We present a feature selection method based on information theoretic measures, targeted at multimodal signal processing, showing how we can quantitatively assess the relevance of features from different modalities. We are able to find the features with the highest amount of information relevant for the recognition task, and at the same having minimal redundancy. Our application is audio- visual speech recognition, and in particular selecting relevant visual features. Experimental results show that our method outperforms other feature selection algorithms from the literature by improving recognition accuracy even with a significantly reduced number of features.