Consumers' emotional requirements, or so-called Kansei needs, have become one of the most important concerns in designing a product. Conventionally, Kansei engineering has been widely used to co-relate these requirements with product parameters. However, a typical Kansei engineering approach relies heavily on the intuition of the person who uses the method in clustering the Kansei adjectives, who may be the engineer or designer. As a result, the selection of Kansei adjectives may not be consistent with the consumers' opinions. In order to obtain a consumer-consistent result, all of the collected Kansei adjectives (usually hundreds) need to be evaluated by every survey participant, which is impractical in most design cases. Therefore, a Kansei clustering method based on a design structure matrix (DSM) is proposed in this work. The method breaks the Kansei adjectives up into a number of subsets so that each participant deals with only a portion of the words collected. Pearson correlations are used to establish the distances among the Kansei adjectives. The subsets are then integrated by merging the identical correlation pairs for an overall Kansei clustering result. The details of the proposed approach are presented and illustrated using a case study on wireless battery drills. The case study reveals that the proposed method is promising in handling Kansei adjective clustering problems.