AbstractHuntington's disease (HD) is a progressive inherited neurodegenerative disorder, causing involuntary movement and cognitive problems, severely affecting the quality of life. Controlling upper limb function is a core feature of daily activity and can prove problematic for people with HD. The Money Box Test (MBT) has been developed with a purpose of quantifying the involuntary movement frequently seen in people with HD. In this research, wearable and highly sensitive accelerometers are used to collect the acceleration of the hands and chest during the performance of the MBT. Using this data, a new approach is proposed to automatically classify the participants into two classes, healthy and HD, on the basis of the time series accelerometer data. A set of 90 time domain features is extracted from the accelerometer data, a feature selection technique is used to analyse the feature significance and to reduce the dimensionality of the dataset, and finally an SVM classifier is used to classify subjects into healthy and HD classes. The data of seven healthy controls and 15 HD patients are used in this study. The highest accuracy with the most significant eight features is 86.36% with the sensitivity and the specificity values being 87.50%, and 83.33% respectively.