The need for efficient content-based image retrieval system has increased hugely. Efficient and effective retrieval techniques of images are desired because of the explosive growth of digital images. content based image retrieval (CBIR) is a promising approach because of its automatic indexing retrieval based on their semantic features and visual appearance. The similarity of images depends on the feature representation.However users have difficulties in representing their information needs in queries to content based image retrieval systems. In this paper we investigate two methods for describing the contents of images. The first one characterizes images by global descriptor attributes, while the second is based on color histogram approach.To compute feature vectors for Global descriptor, required time is much less as compared to color histogram. Hence cross correlation value & image descriptor attributes are calculated prior histogram implementation to make CBIR system more efficient.The performance of this approach is measured and results are shown. The aim of this paper is to compare various global descriptor attributes and to make CBIR system more efficient. It is found that further modifications are needed to produce better performance in searching images.