In fabric productions, real time defect detection systems are needed to detect the surface defects. There is currently no real time defect detection system in knitting fabric production. In this paper, a real time fabric defect detection system is developed and is tested on circular knitting machine. Textural features of fabric image are extracted based on Fourier transform. These textural features are seven and are calculated from the horizontal and vertical directions of Fourier frequency spectrum of the fabric image. The performance of the proposed method is firstly evaluated off-line through experiments based on comprehensive fabric database. The proposed method obtains superior performance, which also proves its utility in real-time inspection. Secondly, a real time machine-vision system has been designed for an efficient detection of the fabric defects under industrial conditions. Real time defect detection system is tested automatically by analyzing fabric images captured by a line scan camera. Experimental results show that the proposed detection model can successfully detect common circular knitting fabric defects.