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In the food industry there are various foodstuffs in the form of grains. Of particular importance is rice, being a commodity crop. The ability to recognize defining characteristics for identification is desirable as fraudulent mislabeling of rice grain varieties is a growing problem. In the present work a digital imaging approach has been devised in order to investigate different types of characteristics to identify different rice varieties. Eight different common rice varieties were used in tests for defining features. These include existing standards for grain length and aspect ratio features, but also successfully show the effectiveness of compactness as a feature. A novel texture feature is also shown to be able to distinguish brown and milled rice in greyscale images. All of these techniques are employed in an inexpensive imaging system that is non-intrusive and nondestructive. A highly effective yet simple imaging setup and processing system is established, permitting image acquisition, image processing, and feature extraction. Features are assessed using unsupervised clustering techniques, showing the dissimilarity between different varieties to a degree that would allow successful identification.