In the present study, the CCC shade sorting method was employed with CMC(2:1) color difference formula on the colorimetric data (CIEL*a* b*) of 37 fabric color sets. The k-means non-hierarchical clustering technique was also combined with the CCC shade sorting method to increase its efficiency. The results of this combined method showed a slightly better performance, as compared with the CCC method. Also, a new proposed shade sorting method by the application of principal components analysis (PCA) technique was used to identify and remove the outliers in each of the color sets. The results of separating the outliers showed that although the diameter of group criterion was improved significantly, the number of groups, the number of singleton groups, and the number of groups with low samples were increased considerably. Finally, in a second new proposed shade sorting method, PCA was used as a data reduction tool on the colorimetric data of the 37 color sets. Then, the two first principal components in combination with a k-means clustering technique were used for the clustering of the samples in each color set. The results of this second new proposed method were found to be similar to the CCC method considering number of group and fabric consumption criteria. The second new proposed method revealed a moderately worse result, with regard to the diameter of group criterion, than the CCC method.