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Measuring the likeness between data in different ways is an important part of pattern recognition and, over the years, many such measures have been developed. This paper proposes an asymmetric measure of likeness based on the concept of context dependent divergence. This is used to construct a numerical descriptor for images and, in conjunction with fuzzy sets, to develop a supervised learning algorithm. When applied to the problem of handwritten digit recognition, the algorithm produces promising and highly accurate results.