Semantic scene classification, the process of categorizing photographs into a discrete set of classes using pattern recognition techniques, is a useful ability for image annotation, organization and retrieval. The literature has focused on classifying outdoor scenes such as beaches and sunsets. Here, we focus on a much more difficult problem, that of differentiating between typical rooms in home interiors, such as bedrooms or kitchens. This requires robust image feature extraction and classification techniques, such as SIFT (scale-invariant feature transform) features and Adaboost classifiers. To this end, we derived SIFT keypoint histograms, an efficient image representation that utilizes variance information from linear discriminant analysis. We compare SIFT keypoint histograms with other features such as spatial color moments and compare Adaboost with support vector machine classifiers. We outline the various techniques used, show their advantages, disadvantages, and actual performance, and determine the most effective algorithm of those tested for home interior classification. Furthermore, we present results of pairwise classification of 7 rooms typically found in homes.