Group Technology (GT) and Cellular Manufacturing System (CMS) portray a significant role in improving productivity for manufacturing organisations. GT is a technique that identifies homogeneous parts and further clusters them into part families based on their manufacturing designs, attributes and geometric shapes. A successful implementation of GT can eventually shrink the engineering costs, facilitate cellular manufacturing, quicken product development, enhance costing accuracy, etc. A major prerequisite in implementing GT is the recognition of part families. Part Coding Analysis proved to be an evolutionary technique in establishing such part similarities. Clustering analysis is practiced in CMS as a competent methodology to facilitate the machine-part grouping problems. In this research, different clustering techniques such as Single Linkage Clustering, Complete Linkage Clustering and K-means Clustering Algorithms have been adopted to investigate the nature of similarities and to describe the effectiveness of the techniques in solving the part family problem. Besides, Principal Component Analysis and Particle Swarm Optimization were used to form competent part families.