In some cases, customer classiÿcation is important for the development of advanced logistical distribution strategies in response to the growing complexity in business logistical markets. This paper presents a new approach that can be employed to cluster customers before executing eet routing in logistical operations. The proposed approach is developed on the basis of fuzzy clustering techniques, and involves three sequential mechanisms including: (1) binary transformation, (2) generation of a fuzzy correlation matrix, and (3) customer clustering. Such a customer clustering method should be performed prior to vehicle dispatching and routing in the process of goods distribution. The proposed methodology clusters customers on the basis of their demand attributes, rather than the static geographic property which is considered extensively in most published vehicle routing algorithms. In addition to methodology development, a case study was conducted to demonstrate the potential advantages of the proposed fuzzy clustering based method. It is expected that this study can stimulate more research on time-based logistics control and management.