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Handwriting is the most effective way by which civilized people speaks. Devanagari is the basic Script widely used all over India. Many Indian languages like Hindi, Marathi, Rajasthani are based on Devanagari Script. In the proposed work multistage approach i.e. an artificial neural network based classifier and statistical and structural method based feature extraction method has been employed for the recognition of the script. Optical isolated Marathi words are taken as an input image from the scanner. An input image is preprocessed and segmented. The key step is feature extraction, features are extracted in terms of various structural and statistical features like End points, middle bar, loop, end bar, aspect ratio etc. Feature vector is applied to Self organizing map (SOM) which is one of the classifier of an artificial neural Network.SOM is trained for such 3000 different characters collected from 500 persons. The characters are classified into three different classes. The proposed classifier attains 98% - 99% accuracy except special characters.