A novel system for the recognition of head movement gestures used to convey non-manual information in sign language is presented. We propose a framework for recognizing a set of head movement gestures and identifying head movements outside of this set. Experiments show our proposed system is capable of classifying three different head movement gestures and identifying 15 other head movements as movements which are outside of the training set. In this paper we perform experiments to investigate the best feature vectors for discriminating between positive a negative head movement gestures and a ROC analysis of the systems classifications performance showed an area under the curve measurement of 0:936 for the best performing feature vector.