A large number of statistical measures have been postulated for the description and discrimination of textures. While most are useful in some situations, none are totally effective in all of them. An alternative approach is to learn which measures are best for particular circumstances. In this paper the distributed learning system of constraint motion is used to learn relevant texture descriptors from a set of well-known first and second order grey-level statistics. Using this system, a network of distributed units partitions itself into sets of units that detect one and only one of the given classes of textures. Each of these sets is further partitioned into individual units that detect natural subtypes of these texture classes, ones which do not necessarily produce the same types of statistics at the local level. Together, these units form a network capable of determining the texture classification of an image.