Abstract: Quality inspection is one of the major problem for fabric manufacturers in textile industries. Textile manufacturing is a process of converting various types of fibers into yarn, woven then into fabric. Weaving is a process of interlacing two distinct yarns namely warp and weft. A fabric fault is any abnormality in the fabric that hinders its acceptability by the user. At present, the fault detection is done manually after production of a sufficient amount of fabric. The nature of work is very dull and repetitive. There is a possibility of human errors with high inspection time in manual inspection, hence it is uneconomical. This study proposed a computer based inspection system for identification of defects in the woven fabrics using image processing and Artificial Neural Network (ANN) with benefits of low cost and high detection rate. The defects consist of hole, stain, warp float and weft float. The inspection system first acquires high quality vibration free images of the fabric. Then, the acquired images are first normalized and preprocessed using image processing techniques then the preprocessed image is converted into binary images based on the threshold value. From the binary image features are extracted and these extracted features are given as input to the Artificial Neural Network (ANN) which uses Back Propagation algorithm to calculate the weighted factors and generates the output. The ANN is trained by using 115 defect free and defected images.