Multivariate Statistical Classifiers for Extracting Discriminant Information in Limited Sample Size Problems In classification problems, when the number of examples per class is less than or comparable to the dimension of the feature space, the performance of statistical pattern recognition techniques tends to deteriorate. This problem, called the ‘limited sample size problem’, is indeed quite common nowadays, especially in image recognition applications. In this talk, I will present a couple of ideas of using multivariate statistical classifiers to identify and analyse the most discriminating hyper-planes separating two populations. The goal is to analyse all the image features simultaneously rather than segmented versions of the data separately, feature-by-feature, or distinct models for texture and shape information. To demonstrate the performance of these statistical pattern recognition approaches I show some experimental results on medical data composed of 3D magnetic resonance images and on frontal 2D face images.