Unlike age-related dementia, Alzheimer's disease is more progressive and causes rapid deterioration in a patient's cognitive functions. Before its first clinical manifestation, it is evident that the damaging brain process has already been commenced much earlier in life. This asymptomatic period could have spanned as long as a decade or more. Although there is not yet an ultimate cure for the disease, the sooner it is diagnosed, the more chance that available therapeutic measures could improve the patient's quality of life. Standard medical questionnaire and medical imaging are the principal means of identifying early Alzheimer's disease. Despite a great effort having been made in analyzing structural atrophy in the human brain by using CT and MRI, the recent attempts have reached high accuracy and precision but relatively poor sensitivity. Functional imaging such as PET is of much lower spatial resolution but promising modality taken to elevate this limitation. This paper presents a classification method for early detection of the disease from PET scans drawn from the Thai population. However, instead of conventional structural analysis, this study performed clustering on unwrapped signals, transformed from imaging data by using Mel-Frequency Cepstral Coefficients (MFCC), by a generic Support Vector Machine (SVM) classifier. The experimental results reported herein indicate that, with the optimal MFCC order, the proposed method could identify subjects with Alzheimer's from controls, with high accuracy, precision, and specificity. With a cross-validation ratio of 8:2 and a linear SVM kernel, the classification accuracy, sensitivity, and specificity were 96.51, 93.98, and 97.77, respectively, and increased as the MFCC orders.