Hybrid Assistive Limb (HAL) is an assistive technology device for supporting physically disabled persons by understanding the percentage of their disability. This work aims to design and develop a HAL based on Electromyogram (EMG) signals. The EMG signal is a biomedical signal that measures electrical currents generated by muscles. These signals can be used for clinical/biomedical applications if advanced methods for detection, decomposition, processing, and classification are available. The pattern of the EMG signal produced may differ depending on the activity of the muscle movement. Four types of biceps muscle activities are identified using the signal pattern generated from raw surface EMG data. Threshold detection method and pattern recognition method were carried out and it is found that pattern recognition method is more generalized method for classification as threshold method is user dependent. The overall classification rate of about (80-83) % obtained using LDA and a classification rate of more than 90% obtained using ANN. Control commands for a stepper motor used for driving artificial limb are developed from the classified EMG signal and stepper motor control is achieved through computer parallel port.