  An artificial neural network is usually treated as a whole system, being
characterized by its ground state (the global minimum of the energy
functional), the set of fixed points, their basins of attraction, etc. However,
it is quite natural to suppose that a large network may consist of a set of
almost autonome subnets. Each subnet works independently (or almost
independently) and analyzes the same pattern from other points of view. It
seems that it is a proper model for the natural neural networks. We discuss the
problem of decomposition of a neural network into a set of weakly coupled
subnets. The used technique is similar to the method for {\it the extremal
grouping of parameters}, proposed by E.M.Braverman (1970).
