Optical Character reader (OCR) systems can be used in digitizing print documents. OCR texts are generated in the process of digitizing print documents. Usually these texts need to be indexed and organized to simplify their access and retrieval. This can be done by the use of automatic classification techniques. However it is currently impossible for OCR technology to recognize all characters with an accuracy of 100%. Furthermore it is not known whether part of speech (POS) analysis contributes to proper OCR texts representation in a discriminative way. Conventionally, the bag-of-words approach is used in OCR text classification. In this paper we experimentally evaluated POS analysis on OCR texts to formulate an informative feature set. Empirical results indicate that the combination of suitably selected POS improved classification performance of OCR texts.