The goal of this research is to develop a gait monitoring system for patients with Parkinson's disease (PD) using wearable sensors. To achieve this objective, the first step of our work is to identify the most significant features that would best distinguish between subjects with PD and healthy control subjects. Here, various gait features were extracted using data obtained from an online database (Physionet) and further analyzed to find the most significant features that would provide the best discrimination between the two groups. The statistical analysis of variance (ANOVA) test was conducted to differentiate the subjects based on the values of the mean and pattern classification was carried out using the Linear Discriminant Analysis (LDA) algorithm. The results show that a distinct set of gait features (step distance, stance and swing phases) contributed significantly in achieving a better classification accuracy rate.