PurposeThe control charts are used in many production areas because they give an idea about the quality characteristic(s) of a product. The control limits are calculated and the data are examined whether the quality characteristic(s) is/are within these limits. At this point, it may be confusing to comment, especially if it is slightly below or above the limit values. In order to overcome this situation, it is suitable to use fuzzy numbers instead of crisp numbers. The purpose of this paper is to demonstrate how to create control limits ofX¯-R control charts for a specified data set of interval type-2 fuzzy sets.Design/methodology/approachThere are methods in the literature, such as defuzzification, distance, ranking and likelihood, which may be applicable for interval type-2 fuzzy set. This study is the first that these methods are adapted to theX¯-R control charts. This methodology enables interval type-2 fuzzy sets to be used inX¯-R control charts.FindingsIt is demonstrated that the methods – such as defuzzification, distance, ranking and likelihood for interval type-2 fuzzy sets – could be applied to theX¯-R control charts. The fuzzy control charts created using the methods provide similar results in terms of in/out control situations. On the other hand, the sample points depicted on charts show similar pattern, even though the calculations are different based on their own structures. Finally, the control charts obtained with interval type-2 fuzzy sets and the control charts obtained with crisp numbers are compared.Research limitations/implicationsBased on the related literature, research works on interval type-2 fuzzy control charts seem to be very limited. This study shows the applicability of different interval type-2 fuzzy methods onX¯-R control charts. For the future study, different interval type-2 fuzzy methods may be considered forX¯-R control charts.Originality/valueThe unique contribution of this research to the relevant literature is that interval type-2 fuzzy numbers for quantitative control charts, such asX¯-R control charts, is used for the first time in this context. Since the research is the first adaptation of interval type-2 fuzzy sets onX¯-R control charts, the authors believe that this study will lead and encourage the people who work on this topic.