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In the last half century, the English character recognition was studied and the results were of such type that's it can produce technology driven applications. But the same approach cannot be used in case of Indian languages due to the nature of complication in terms of structure and computation. "Hindi" the national language of India (written in Devanagri script) is world's third most popular language after Chinese and English. Devanagri handwritten character recognition has got lot of application in different fields like postal address reading, cheques reading electronically. There are several Handwritten numeral recognition have been proposed and evolved during last few decades. But robustness and accuracy of such system is still a issue due to variety of writing patterns, size, slant, ink, and writing style. So In this paper, a novel approach for Devanagari handwritten numerals recognition based on global and local structural features is proposed. Probabilistic Neural Network(PNN) Classifier is used to classify the Devanagari numerals separately.