Due to variation in economic value of different varieties of rice, reports indicating the possibility of mixing different varieties on the market. Applying machine vision techniques to classify rice varieties is a method which can increase the accuracy of classification process in real applications. In this study, several morphological and textural features of rice seeds’ images were examined to evaluate their efficacy in identification of three Iranian rice varieties (Tarom, Fajr, Shiroodi) in their mixed samples. On the whole, 666 images of rice seeds (222 images of each variety) were acquired at a stable illumination condition and totally, 17 morphological and 41 textural features were extracted from seeds images. Principal component analysis (PCA) method was employed to select and rank the most significant features for the classification. Subsequently, the MLP neural network classifier was employed for classification of rice varieties in the mixed bulks of three and two varieties, using top selected features. The network was three-layered feed forward type and trained using two training algorithms (BB and BDLRF). The classification accuracy of 55.93, 84.62 and 82.86 % for Fajr, Tarom and Shiroodi, 86.96 and 93.02 % for Fajr and Shiroodi, 86.84 and 96.08 % for Tarom and Shiroodi and 91.49 and 95.24 % for Fajr and Tarom were obtained in test phase, respectively.

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