A human perceives a set of feature points (FPs) as a cluster when he/she finds a mass of collected FPs in a sample space. We have been trying to develop a clustering technique working like clustering in human perception. For such a clustering technique, we introduce the concept of perceptual position in this paper. The perceptual position is based on the assumption that human perception of relative positions among FPs changes depending on the arrangement of FPs around those FPs. To implement a perceptual position, each FP is encoded by a fuzzy set. We then describe a clustering technique using perceptual positions. Computational experiments were carried out to determine the effectiveness of the clustering technique using perceptual position, and the results showed that clustering by the technique using perceptual position is more compatible with clustering by human subjects than is clustering using a conventional fuzzy c-means (FCM) algorithms.