Gesture recognition refers to the mathematical interpretation of human motions using a computing device. With the increasing use of technology, hand-gesture recognition has become an essential aspect of Human-Machine Interaction (HMI), allowing the machine to capture the user's intention and respond accordingly. It is a fundamental tool to enable novel interaction paradigms in numerous applications, such as robotics, prosthetic control, healthcare and sign language. Several technologies are currently available to detect gestures. Data acquisition systems based on surface ElectroMyoGraphic (sEMG) signals, collected from non-invasive electrodes on the skin of the area of interest, are extensively used, especially in biomedical research. This thesis work aims to develop and validate a seven-channel sEMG armband for hand gesture recognition.