An optical character recognition (OCR) system recognizes either printed or handwritten text. Hence it is required to seperate machine printed text from handwritten text in scanned documents before feeding it to a OCR system. We can discriminate these two types of text word images by their visual impression and shape structures. The intensity values distribution features gives us the visual impression and the shapes can be represented by the structural features. This paper proposes an approach for machine print and handwritten text classification at word level using intensity and shape structural features of scanned text. The proposed method achieved impressive classification efficiency on IAM dataset.