Human action recognition using depth sensors is an emerging technology especially in game console industry. Depth information can provide robust features about 3D environments and increase accuracy of action recognition in short ranges. This paper presents an approach to recognize basic human actions using depth information obtained from the Kinect sensor. To recognize actions, features extracted from angle and displacement information of joints are used. Actions are classified using support vector machines and random forest (RF) algorithm. The model is tested on HUN-3D, MSRC-12, and MSR Action 3D datasets with various testing approaches and obtained promising results especially with the RF algorithm. The proposed approach produces robust results independent from the dataset with simple and computationally cheap features.