In the textile industry, the colour of the products is of great importance, and is one of the major factors influence the customer attraction to the product. The proper formulation and correct colour correction of the products during the colour matching process, increases the quality of the products. However, due to the variety of reasons, such as change in the quality of fibres, fabric structure, temperature, humidity, dye quality, spinning process and the controlling factor of dyeing processes, the colour of a textile product may slightly be altered from batch to batch of the product. Visual and instrumental methods of shade sorting are solutions to identify the difference in the product colour and to sort the products in groups with the acceptable colour difference between the samples in the groups. Because of the lack of enough repeatability, unreliable results, high time consumption and cost of the visual method of shade sorting, it is necessary to use the instrumental methods of shade sorting. An important instrumental method of shade sorting in textile industry is CCC technique, which is done by the application of CMC(2:1) colour difference formula. Therefore, improving the performance of the CCC technique is of great importance for the practitioners in textile industry. In the present research, the CCC technique is done by using CMC(2:1) and CIEDE2000 colour difference formula on the colorimetric data(L*a*b*) of the samples i 37 groups of the fabric samples made around 9 standard colour points in CIELAB colour space. The groups were had been sorted visually by the colour experts. Also, the K-means technique, which is a non-hierarchical clustering technique, was combined with the CCC technique in order to increase the performance of the shade sorting. Moreover, the effect of applying a PCA technique on the colorimetric data of the samples in each of the sorted groups to remove the pert samples was examined. Different criteria were used to compare the performance of the shade sorting techniques performed in the present research. The results show that the application of the CIEDE2000 colour difference formula in the CCC technique improved the technique’s performance in comparison to applying the CMC(2:1) colour difference formula. Also, the combination of the K-means technique with the CCC technique enhances the performance of the shade sorting. Applying the PCA on the colorimetric data of the samples, at the beginning of the shade sorting process, shows ambiguity. It means that some of the criteria to assess the performance of the shade sorting techniques were improved and the other criteria were deteriorated. This shows that the application of the PCA technique depends on the importance of each of the criteria for the end use of the products and /or on the priority made by customers on the criteria.