Alzheimer's disease (AD) is a neurodegenerative disease whose definitive diagnosis is only possible via autopsy. Currently used diagnostic approaches include the traditional neuropsychological tests, and recently more objective biomarkers, such as those obtained from cerebral spinal fluid (CSF), magnetic imaging resonance (MRI), and positron emission tomography (PET). Electroencephalography (EEG), a lower cost and non-invasive alternative, has been previously tried but with mixed success. In this effort, we attempt a more comprehensive analysis and comparison of machine learning approaches using EEG based features to determine diagnostic utility of the EEG. We compared support vector machine (SVM), naïve Bayes, multilayer perceptron (MLP), CART trees, k-nearest neighbor (kNN), and AdaBoost on various sets of features extracted from event related potentials (ERP) of the EEG. Our analysis suggests that there is indeed diagnostically useful information in the ERP of the EEG for early diagnosis of AD.