In India, demand for various fruits and vegetables are increasing as population grows. Automation in agriculture plays a vital role in increasing the productivity and economic growth of the Country, therefore there is a need for automated system for accurate, fast and quality fruits determination . Researchers have developed numerous algorithms for quality grading and sorting of fruit. Color is most striking feature for identifying disease and maturity of the fruit. In this paper; efficient algorithms for color feature extraction are reviewed. Then after, various classification technique are compared based on their merits and demerits . The objective of the paper is to provide introduction to machine learning and col or based grading algorithms, its components and current work reported on an automatic fruit grading system. However, classification process is challenging for images captured in natural environment due to the existence of non – uniform illuminati on . Different illuminations produce different intensity on the object surface and thus lead to inaccurate classification. Therefore, this study focuses on the improvement of development of classification model for images captured in natural environment. This study has developed a neural network (NN) model that is able to classify objects based on their surface color. Hence the KNN classification method and CART also implemented to have a better comparison result.