Cancer of the colon is among the leading types of cancer. Worldwide, colorectal cancer is considered the third most common neoplasm. Similar to other types of cancers, early detection of colon cancer is the key to a successful treatment. Traditionally, pathologists use microscope to examine histopathological images of biopsy samples taken from patients and make judgments based on their professional expertise. Typically, a pathologist would make observations on some key features in the image and subsequently be able to classify whether or not the tissue under examination contains abnormality. Since this procedure is performed by a human expert, it is therefore subject to inconsistencies due to factors that might affect human performance. To overcome this problem, it has been proposed to use computers to aid in the analysis of medical images, such as histopathological images of colonic tissues. One of the strongest signs of abnormal cellular growth is abundance of DNA material in the nuclei, a condition known as hyperchromasia. Another sign is a ‘chaotic’ appearance of tissue usually observed during histological viewing. Cancer is known to be characterized by abnormal and excessive proliferation of cells and larger-than-usual nuclei. These effects are visually manifested by a darkening of the regions with excess DNA due to reaction to staining and a loss of structural order in the tissue. In this paper, pixel intensity and the presence of circular formations are examined separately as discriminating image features to distinguish between normal and abnormal samples. The images used in this study were derived from slides and cases randomly chosen from surgical pathology files of a hospital, previously diagnosed by pathologists as colonic adenocarcinoma, adenomatous polyps from the colon, as well as resection planes of the colonic resections without tumor to serve as controls. All samples were stained with hematoxylin and eosin and were taken at 400x magnification. The results showed very promising results.