Feature extraction is a key function in various image processing applications. A feature is an image characteristic that can capture certain visual property of the image. Texture is an important feature of many image types, which is the pattern of information or arrangement of the structure found in a picture. Texture features are used in different applications such as image processing, remote sensing and content-based image retrieval. These features can be extracted in several ways. The most common way is using a Gray Level Co-occurrence Matrix (GLCM). GLCM contains the second-order statistical information of neighboring pixels of an image. Textural properties can be calculated from GLCM to understand the details about the image content. However, the calculation of GLCM is very computationally intensive. In this thesis, an FPGA accelerator for fast calculation of GLCM is designed and implemented. We propose an FPGA-based architecture for parallel computation of symmetric co-occurrence matrices. Experimental results show that our approach improves 2x up to 4x the processing time for simultaneous computation of sixteen co-occurrence matrices.