In this project, the task of architecture classification for monuments and buildings from the Indian subcontinent was explored. Five major classes of architecture were taken and various supervised learning methods, both probabilistic and nonprobabilistic, were experimented with in order to classify the monuments into one of the five categories. The categories were: ’Ancient’, ’British’, ’IndoIslamic’, ’Maratha’ and ’Sikh’. Local ORB feature descriptors were used to represent each image and clustering was applied to quantize the obtained features to a smaller size. Other than the typical method of using features to do an image-wise classification, another method where descriptor wise classification is done was also explored. In this method, image label was provided as the mode of the labels of the descriptors of that image. It was found that among the different classifiers, k nearest neighbors for the case of descriptor-wise classification performed the best.