There is an absolute need for the robust quality inspection system, which can take on the new challenges and overcome the human limitations to inspect the fabric production consistently. To address this gap, computer vision based techniques are employed by enterprises. Some of the beneficent manufacturers include ceramic, circuit board printer, paper printer and textiles. Excellent work has been done in the yarn and weaving production sections of textile industry, however fabric printing monitoring is being explored by the researchers. The referential approach is mostly adopted to monitor fabric printing for defect detection. It works by acquiring an error-free image and registering this image with subsequent images captured during the production. In this method, the major time-consuming problem is to find the design repeat in the sample image before further processing. The researchers suggested some methods like distance matching in RGB space, photo encoders, genetic algorithm along with recursive splitting, Fuzzy C-means clustering, and cross correlation algorithm. There are certain limitations with these methods, for instance, sensitive to small deformation, required extra hardware prone to mechanical fault and it is expensive in calculations. In this research, a method for finding the design repeat is proposed. The method is tuned up according to the textile printing domain. Since the fabric moves in one direction during printing process, therefore the image registration can be confined to the same direction. To further speed up the process, the image registration method is applied using few initial pixel columns of the reference image with the sample image. Such bunch of selected columns of the reference image is matched with the same number of columns selected from sample image by moving this bunch on the sample image column by column. The maximum matching position is marked as the start of the design repeat. As the repeat size is always fixed, therefore complete design can be extracted from acquired image for defect detection. The experimental results on different fabric designs using the above-mentioned method are promising. Moreover, this technique improves the image alignment speed which enhances defect detection system performance. The most common defect is the displacement or misregistration of a colour. This defect is caused by the misalignment of cylindrical screens of fabric printing machine. As every screen is responsible for a colour, the misaligned screen registers the colour on the incorrect position. It leads to defective production and contributes to a substantial loss of the material and time. Few researchers proposed the feature based solution which are either computationally expensive or do not provide detail information about the defect like location and colour. Further, some techniques depend on the additional algorithms to find defects. Mostly, these methods depend on the accuracy of reference and sample image alignment. So there is always a chance of wrong notification. An improved algorithm for the detection of displacement of a colour during fabric printing is proposed in this thesis. The algorithm concatenates red, green and blue pixel values of the RGB image to represent a colour and later produces a frequency distribution of different colours for both sample and reference images. The colours with low frequencies are removed considering noise. In the next step, colours are grouped depending upon their distances from each other. These colour groups are later used to detect any new colour in the sample image which is actually created by the displacement defect and colour variation. The proposed algorithm successfully detects displacement and colour variation defects when it is tested using different flawed printed fabric images. The result shows that the proposed method has almost same accuracy as stat of the art algorithm and more helpful to detect the colour displacement, and it can also overcome the shortcoming of repeat finding process.