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Cellular manufacturing consists of grouping similar machines in cells and dedicating each of them to process a family of similar part types. In this paper, grouping parts into families and machines into cells is done in two steps: first, part families are formed and then machines are assigned. In phase one, weighted similarity coefficients are computed and parts are clustered using a new self-organizing neural network. In phase two, a linear network flow model is used to assign machines to families. To test the proposed approach, different problems from the literature have been solved. As benchmarks we have used a Maximum Spanning Tree heuristic.