A cell-formation approach based on association rule induction is developed to find the effective configurations for cellular manufacturing systems. To gain the benefits of flexibility and efficiency, the manufacturing system is decomposed into several manageable subsystems by categorizing similar parts into part families and disparate machines into cells. It is advantageous to find the important associations among machines such that the occurrence of some machines in a machine cell will cause the occurrence of other machines in the same cell. Relationships among machines can be found from the process database by inducting association rules. By applying association rules to cell-formation problems, certain sets of machines (machine groups) that frequently process some parts together can be inducted. A data-mining technique referred to as association rule induction is used herein to find the association rules among machines from the process database. Seventeen data sets of various size and complexity were used to evaluate the effectiveness of the proposed cell-formation algorithm based on association rule induction. The performance of the proposed approach is compared with several existing techniques. From the computational results, the proposed approach shows its ability to find quality solutions.