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Optical Character Recognition is an appealing field of work for research where an image containing text is given as input and text in the image is translated into an editable format. This paper proposes Meetei/Meitei Mayek Handwritten Digit Recognition System where an Isolated Handwritten Meetei Mayek Digit Database consisting of 10000 plus digits has been developed. This proposed Handwritten Meetei Mayek Digit Recognition System is an important component of Manipuri Meetei Mayek Optical Character Recognition system which is under development. For feature extraction, we have used State-of-Art techniques—Histogram of Oriented Gradients and Bag of Features Descriptor for Speeded Up Robust Features. Five classifiers have been employed for classification viz. Support Vector Machine, with Linear, Polynomial and Radial Basis Function kernels, K-Nearest Neighbours and Bootstrap Aggregating and compared in terms of accuracy. Support Vector Machine using Radial Basis Function Kernel has found to achieve the recognition accuracy with the highest value compared with the other classifiers with the extracted Histogram of Oriented Gradients and Bag of Features for Speeded Up Robust Features.