In this study a partitional clustering technique is proposed. Our proposal is a variant of the c-Means algorithm that replaces its traditional minimum-distance classifier by a classifier based on associative memories. The variant was compared against the original version by applying both techniques to three datasets belonging to the UCI Machine Learning Repository: Iris, Wine and Pima Indian Diabetes. As a comparison criterion, an intracluster-spread index was used. Results obtained in experimental tests show that, when applied to certain databases, the PAC-Means technique overcomes to the c-Means algorithm.