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Video editors are frequently required to access sections of a video sequence which contain a particular scene. This may be regarded as an image retrieval-by-content problem where the user wishes to select images from within a large database according to a measure of similarity to a target. We present an intelligent video editing system based on a neural network coding scheme. The transformation learnt by the neural network maps each image into a very compact index which supports rapid fuzzy matching of video images. The neural network is trained using a learning law which produces an information preserving transform. Trained in this way, the node learns features which characterise the distribution of scenes within the video sequence. Each image frame in the sequence is coded with respect to these features.We show how the system performs on a typical sequence of newsreel footage and discuss the factors affecting the performance of both the training and the retrieval mechanism.