Regional brain atrophy is a typical structural symptom of Alzheimer's disease (AD). Magnetic resonance imaging (MRI) scans capture brain structure with high resolution and are often processed with automated segmentation and parcellation algorithm (e.g. Freesurfer) to generate regional measures, like cortical volume, cortical thickness and surface area, which are widely used as inputs in classification algorithms. This study aims to find out which combination of MRI measures and neuropsychological test coupled with different normalization techniques can best predict AD using a proposed multivariate feature selection and classification method. A total of 189 subjects with 60 Alzheimer's disease (AD) and 129 cognitively normal (CN) are included in this study. Freesurfer was used to obtain 34 cortical thickness measures and 35 surface area measures for each hemisphere and 55 regional volumes across the brain. Statistically significant variables selected for each model were used to construct the multi-dimensional space for further classification using a support vector machine (SVM) classifier. Different normalization approaches were explored to gauge if the classification performance could be improved. Results indicate neuropsychological test score contains the most discriminative information among single measure models, and out of the three MRI measures, cortical volume is a better predicator than the other two. Normalization approaches are seen not to enhance the performance much if any. Hierarchical model of neuropsychological test and cortical volumes without normalization yield the best classification accuracy for this study.