One of the best staple foods, rice is widely consumed by the general populace in Asian nations. It's challenging to distinguish between the different varieties of rice. However, this can be avoided by categorizing it according to its shape and morphological features. Main purpose of this paper is to present an image processing-based solution to classify the different varieties of rice for same brand. Artificial Neural Network (ANN), which is the branch of Artificial Intelligence (AI) was used to conduct the classification and become the benchmark in terms of accuracy. The classification of rice grain also determined by morphological features. These morphological features include contrast, correlation, energy, homogeneity, area, eccentricity, extend, minor axis length and major axis length. Nine different varieties of rice are classified and analyzed in this paper. A database is trained by feeding the 180 images consisting of 150 training images and 30 tested images of each variety of rice. Classification analysis is done by comparing the training image with test image. The results of the evaluation of the ANN method showed that the overall value of accuracy obtained for training data was 98% and for tested data was 93.3 %.