In this paper, we propose a new scheme for script and nature identification. The objective is to discriminate between machine-printed/handwritten and Latin/Arabic scripts at word level. It is relatively a complex task due to possible use of multi-fonts and sizes, complexity and variation in handwriting. In the proposed script identification system, we extract features from word images using Co-occurrence Matrix of Oriented Gradients (Co-MOG). The classification is done using k Nearest Neighbors (k-NN) classifier. Extensive experimentation has been carried on 24000 words extracted from standard databases. An average identification accuracy of 99.85% is achieved which clearly outperforms results of some existing systems