This paper describes different feature extraction and matching techniques in designing a Content Based Image Retrieval system. Due to the enormous increase in image database sizes, as well as its vast deployment in various applications, the need for CBIR development arose. This paper outlines a description of the primitive feature extraction techniques like texture, colour and shape. Once these features are extracted and used as the basis for a similarity check between images, the various matching techniques are discussed. In CBIR, each image that is stored in the database has its features extracted and compared to the features of the query image. This is done to retrieve images in the database that are visually similar to the query image. In this paper, five types of CBIR methods such as RVPIRA, SLA, SORA, GIRA and CPRA are used for image retrieval. These classification uses different features extraction for retrieval of images such as Image colour quadratic distance for image histogram, Image Euclidian distance for image wavelet transform; image Hamming Distance and corresponding retrieval Recall and Precision parameters are calculated for each feature. For as to increase retrieval efficiency combinations of these features are used instead of using a single feature for image retrieval.