In this article, the challenge of identifying between Essential and Parkinson's tremor is addressed. To this goal, a clinical analysis was performed, where a number of volunteers including Essential and Parkinson's tremor-diagnosed patients underwent a series of pre-defined motion patterns, during which a wearable sensing setup was used to measure their lower arm tremor characteristics from multiple selected points. Extracted features from the acquired accelerometer signals were used to train classification algorithms, including decision trees, discriminant analysis, support vector machine (SVM), K-nearest neighbor (KNN) and ensemble learning algorithms, for providing a comparative study and evaluating the potential of utilizing machine learning to accurately identify between different tremor types.