In this paper, we have developed algorithm which combine features from an object image to recognize it. Object recognition is the most interesting and challenging area of research due to its importance to a wide range of applications as objects differ in shape, size and color. The descriptors used for feature extraction in the existing methods for object recognition are mostly intensity based. For the recognition of object with varying level of light illumination color descriptors have been used in this paper. The color descriptors used in the object recognition system are based upon the intensity levels of hue (H), saturation (S) and value (V) in terms of their histogram for respective object image. The system works in single object recognition mode with plain background. The features used for recognition are mainly the size (along the boundaries) and HSV intensity values of the object's image. It consists of two key modules, feature extraction and object recognition. In this paper object recognition is accomplished by using two different methods in which the classification of the extracted features of object image are on the basis of artificial neural networks (ANN) and scale invariant feature transform (SIFT) based features. We have adapted the Euclidean distance measure metric algorithm for matching the extracted features of object's image. Finally the results in terms of object detection and recognition rates are calculated on the basis of false rejection ratio (FRR) and false acceptance ratio (FAR).