Huntington's disease (HD) is one of the neural diseases with movement disorders like chorea, ballism, and athetosis. These symptoms play an important role on gait asymmetry, thus gait recordings are considered important resources for studying this disease. Diagnosing HD in its early stages is important and critical. We aimed to discover differences between HD and normal behavior. As the gait is semi-periodic, analyzing frequency could reveal disorders. We made an attempt to extract some proper features using power spectra density. Studies revealed that HD adds high frequencies to the spectrum on all gait phases. Statistical analysis of the gait features showed significant differences between normal and HD groups. At the end, we tried to separate the patients and healthy individuals using a new intelligent mathematical system. An artificial neural network classifier was used for this reason,and our best separation accuracy was 96.6%. This study could be the basis of designing a practical decision support system. This system can diagnose patients at the first stages of the disease, and it also can recommend suspected persons to the specialist.