In this paper， a new method is presented for finding data clusters centroids. This method is proposed based on the concept of electrostatic field in which the centroids are positioned at locations where an electrostatic equilibrium or balance can be achieved. After determining the centroids locations, criteria such as the minimum distance to centroid can be used for clustering data points. The performance of the proposed method is compared with that of the k-means algorithm through simulation experiments. The experimental results show that the proposed algorithm does not suffer from the problems associated with k-means, such as sensitivity to noise and initial selection of centroids, and tendency to converge to poor local optimum.