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Gesture recognition is a difficult task in computer vision due to the numerous degrees of freedom of a human hand. Fortunately, human gesture covers only a small part of the theoretical "configuration space" of a hand, so an appearance based representation of human gesture becomes tractable. A major problem, however, is the acquisition of appropriate labelled image data from which an appearance based representation can be built. In This work we apply self-organising maps for a visualisation of large amounts of segmented hands performing pointing gestures. Using a graphical interface, an easy labelling of the data set is facilitated. The labelled set is used to train a neural classification system, which is itself embedded in a larger architecture for the recognition of gestural reference to objects.