Color texture classification plays an important role in computer vision and has a wide variety of applications. Many methods of color texture analysis have been developed over the years; however, a major problem is that textures in the real world are often not uniform owing to variations in rotation and scale. Additionally, color texture images usually contain noises and uncertainties. According to the literature, intuitionistic fuzzy set (IFS) is helpful in modeling vagueness or uncertainty. Therefore, the purpose of this study is to prove that IFS will be a good approach to improve the classification performance of color texture. We applied six well-known texture descriptors (i.e. LBP, GLCM, LTP, LDiP, LDeP and LTrP) to compare them based on IFS and non-IFS based methods, respectively. Experiments show that the IFS-based method can improve the accuracy by 0.88% to 13.16% compared with the non-IFS-based method. In addition, IFS-based methods are also more robust than non-IFS based on rotation and scaling. This conclusion can be used by texture classification researchers to apply IFS to further improve the performance of their own color texture classification methods.