Prediction of any property of the material has attracted the attention of many scientists all over the world in order to produce better products. Information Technology (IT) field has many applications and plays dominant role in the production of various products in the industry. Knitted fabric should satisfy a number of requirements of consumer. Fabric width is a very important property which affects knitted fabric comfort properties. The deviation from the fabric width will either lead to more consumption of raw material or affect profit of the company. Hence, controlling the width of the fabric has an adverse effect on company’s profit and usage of raw materials. An investigation of the prediction of the width of the single jersey cotton knitted fabric in a fully relaxed state using Data mining technique in Rough set Computational based Priority Prediction Model (RCPPM) is reported. The inputs were yarn count, machine diameter, required GSM, machine gauge, actual yarn count, lea weight, lea strength, twist multiplier, loop length, course per cm, wales per cm, length shrinkage, width shrinkage, and fabric width. The real-time textile dataset consisted of 7,505 single jersey cotton knitted fabric samples. The results showed that the fabric width obtained by using aforesaid model was found to yield very accurate values and compared favourably with the measured ones. This study will lead to the production of the knitted fabric with better comfort and dimensional stability.