Content-based image retrieval (CBIR) is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases. "Content-based" means that the search analyses the contents of the image rather than the metadata such as keywords, tags, or descriptions associated with the image. The term "content" in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself. CBIR is desirable because searches that rely purely on metadata are dependent on annotation quality and completeness. Having humans manually annotate images by entering keywords or metadata in a large database can be time-consuming and not capture the keywords desired to describe the image. The evaluation of the effectiveness of keyword image search is subjective and has not been well-defined. In the same regard, CBIR systems have similar challenges in defining success. In this project, retrieval images are carried out by certain feature extraction methods such as Color Feature extraction, Texture feature Extraction and Shape Feature Extraction has been performed. In Color Feature extraction, Color Histogram, Color Moments and Color Auto Correlogram have been proposed. For Texture Feature Extraction Gabor wavelet and Haar, Wavelet process has been implemented. Finally, for Shape Feature Extraction, Fourier Descriptor, Circularity features have been proposed. The extracted features are then optimized by Co-occurrence matrix, where features are optimized and approximated to relevant features. These features are finally classified with similarity computation Euclidean distance to retrieve the relevant images from the databases.