In this study, a novel method is proposed to build a Resting State fMRI (RS fMRI) classifier to discriminate between healthy controls and data of Essential Tremors (ET) disorder. Distinction between healthy controls and diseased subjects data using RS fMRI is more useful in light of the fact that certain patients suffering from neuropsychiatric disorders may be unable to perform the tasks specified for acquisition. Specifically the neurologic disorder that we consider is ET for the reason that fMRI of this disorder is least explored and hence, functionally affected regions of this disease is not clearly known. Regional Homogeneity (ReHo) feature for healthy controls and ET patients was extracted as a mapping to brain function during resting state. One sample t-test was performed for both normal and patient data and regions with significant ReHo values were procured for both the data. The t-test maps respective to the two data groups, consisting of clusters with significant ReHo values, were used as masks respectively on ReHo maps of each of the groups. These masked ReHo maps were used as features as input to a linear classifier. The performance of the proposed scheme for classification of Healthy controls and ET was evaluated and the resulting generalization rate of the classifier was 100% for a dataset consisting of 11 samples in both the groups. The performance of the proposed masking technique remains to be evaluated with a dataset consisting of a large number of samples for ET and Healthy controls.