Analysis of Wireless Capsule Endoscopy (CE) images has become a very active area of research since this novel technology enabled access to previously inaccessible areas of the gastrointestinal tract, especially the small intestine. Art has investigated automatic segmentation of organ boundaries, detection of lesions and bleeding as well as other supervised and unsupervised analysis. However, all of this art has focused on treating the images as individual and independent observations that contribute towards a unique and separate decision. Given the overlap between the images, this is clearly not the case. A human, by contrast, performs assessment by combining the information seen in all neighboring views of the anatomy in a study. This article makes two significant contributions. Towards combining information from multiple images, we propose a supervised classification approach using an HMM framework. Secondly, we use a weak (k-NN) classifier to prototype and evaluate such a framework for regions of the GI tract containing polyps. The combined framework significantly improves the performance of the individual classifier and experiments show promising performance with accuracy 0.9.