Abstract Machine cells formation is the process of identifying families of similar parts (part-families) and forming the associated machine cells such that one or more part-families can be processed within a single cell. Among approaches to the machine cells formation problem the similarity coefficient method (SCM) has the capability of incorporating manufacturing data such as production volume, sequence of operations, and processing times into the machine cells formation process. Single Linkage Clustering (SLINK) and Average Linkage Clustering (ALC) are two widely used clustering techniques based on SCM. While SLINK is simple and has minimal computational requirement, it may generate machine cells in which a large number of machines are far apart in terms of similarity. ALC, on the other hand, overcomes this problem but requires more computations. This paper discusses the application of these two techniques to the machine cells formation problem.