An emerging clinical priority for the treatment of Alzheimer’s disease (AD) is the implementation of therapies at the earliest stages of disease onset. All AD patients pass through an intermediary stage of the disorder known as Mild Cognitive Impairment (MCI), but not all patients with MCI develop AD. By applying computer based signal processing and pattern recognition techniques to the electroencephalogram (EEG), we were able to classify AD patients versus controls with an accuracy rate of greater than 80%. We were also able to categorize MCI patients into two subgroups: those with EEG Beta power profiles resembling AD patients and those more like controls. We then used this brain-based classification to make predictions regarding those MCI patients most likely to progress to AD versus those who would not. Our classification algorithm correctly predicted the clinical status of 4 out of 6 MCI patients returning for 2 year clinical follow-up. While preliminary in nature, our results suggest that automated pattern recognition techniques applied to the EEG may be a useful clinical tool not only for classification of AD patients versus controls, but also for identifying those MCI patients most likely to progress to AD.