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Recognition of human action is a daunting challenge considering action sequences' embodied and dynamic existence. Recently designed material depth sensors and the skeleton estimation algorithm have developed a renewed interest in human skeletal action recognition. This paper performed human action recognition by using a novel SNSP descriptor that acquired complex spatial information among all skeletal joints. In particular, the proposed SNSP determines combined and unite details using the prominent joint. Our features are calculated using the standard normal, slope, and parameter space features. The neck is proposed as a super-joint, SNSP is utilizing features and a prominent joint. We evaluate the proposed approach on three challenging action recognition datasets i.e., UTD Multimodal Human Action Dataset, KARD- Kinect Activity Recognition Dataset, and SBU Kinect Interaction Dataset. The experimental results demonstrate that the proposed method outperforms state-of-the-art human action recognition methods.