State-of-the-art technologies in the fields of computer vision and machine learning led the automatic recognition of malignant structures in histopathology images. More than often, such structures are reported to be found in glands, where different morphological characteristics indicate the existence of a variety of adenocarcinomas, including prostate, breast, lung and colon cancer. Classification of images containing glandular representations in different cancer types can be performed in the whole image by the utilization of a combination of local and global features. The proposed methodology involves the exploitation of a notion utilized often in text mining called Bag of Words and employed in the service of Computer Vision with the name of Bag of Visual Words (BOVW) for the development of a retrieval and classification system for pathology images. The paper discusses the technical details of implementation, the enhancement of the BOVW technique, while some initial results using public datasets are presented.