The study of the literature includes studies performed on hybrid sensor systems for prosthetic hand control between 2014 and 2020. The goal is to integrate different sensors technologies, such as computer vision, mechanomyography, near-infrared spectroscopy, and electromyography (EMG). Moreover Closed-loop control systems, hierarchical control architectures, and multi-classifier systems are notable innovations. Research shows that novel strategies to improve classification accuracy include competency functions, adaptive tuning, and dynamic ensemble selection. Accuracy and robustness are significantly increased in hybrid systems that integrate MMG and EMG sensors, especially when there are malfunctioning channels. The development of multi-modal sensing devices that combine MMG, NIRS, and EMG tackles issues associated with muscle fatigue and offers improved performance for the human-machine interface (HMI). The analysis highlights the value of multi-modal methods for controlling prosthetic hands and suggests possible areas for further investigation. Ongoing developments in the subject are demonstrated by the integration of various sensors and the investigation of small and affordable pattern recognition systems. All things considered; the literature study offers insightful information about how hybrid sensor systems for prosthetic hand control have evolved.