Abstract : . The hippocampus is affected at an early stage in the development of Alzheimer's disease (AD). Using brain Magnetic Resonance (MR) images, we can investigate the effect of AD on the morphology of the hippocampus. Statistical shape models (SSM) are usually used to describe and model the hippocampal shape variations among the population. We use the shape variation from SSM as features to classify AD from normal control cases (NC). Conventional SSM uses principal component analysis (PCA) to compute the modes of variations among the population. Although these modes are representative of variations within the training data, they are not necessarily discriminant on labelled data. In this study, a Hotelling's T2 test is used to qualify the landmarks which can be used for PCA. The resulting variation modes are used as predictors of AD from NC. The discrimination ability of these predictors is evaluated in terms of their classification performances using support vector machines (SVM). Features extracted from the SSM model built on the reduced subset of landmarks lead to an improved discrimination between the groups.