Background: Alzheimer’s disease (AD) is the most dominant type of dementia that has not been treated completely yet. Few Alzheimer‘s patients are correctly diagnosed on time. Therefore, diagnostic tools are needed for better and more efficient diagnoses. Objective: This study aimed to develop an efficient automated method to differentiate Alzheimer’s patients from normal elderly and present the essential features with accurate Alzheimer’s diagnosis. Material and Methods: In this analytical study, 154 Magnetic Resonance Imaging (MRI) scans were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, preprocessed, and normalized by the head size for extracting features (volume, cortical thickness, Sulci depth, and Gyrification Index Features (GIF). Relief-F algorithm, t-test, and one way-ANOVA were used for feature ranking to obtain the most effective features representing the AD for the classification process. Finally, in the classification step, four classifiers were used with 10folds cross-validation as follows: Gaussian Support Vector Machine (GSVM), Linear Support Vector Machine (LSVM), Weighted K-Nearest Neighbors (W-KNN), and Decision Tree algorithm.