Hand amputation can dramatically affect the capabilities of a person. Improving the functionality of robotic prosthetic hands is thus a challenge. The integration of advanced prosthetic and robotic technologies with functional amputations may bring to reality the non-invasive natural control of robotic hand prostheses in a near future. Scientific research and prosthetic market are rapidly advancing towards the natural control of dexterous robotic prosthetic hands. Myoelectric hand prostheses with many degrees of freedom are commercially available and recent advances in scientific research suggest that their natural control can be performed in real life through pattern recognition and the integration of multimodal data. However, robustness is still not sufficient to transfer scientific results to a real life. In this work we describe the Ninapro (Non Invasive Adaptive Prosthetics) database, which is aimed to study the relationships between sEMG, hand movement, force and clinical parameters. The data are publicly available to worldwide research groups. The Ninapro database allowed to obtain several important results including: showing that up to 11 hand movements can be recognized without any training in amputated subject; showing that multimodal data can strongly improve movement recognition; showing that several clinical parameters (including remaining forearm percentage and phantom limb sensation) are related to the capability of amputees to control the remnant muscles in the stump. 20 The Ninapro results, in combination with other scientific literature achievements, suggests that future "functional amputation" surgery procedures may better integrate with the prosthetic robotic limbs and contribute to solve natural control problems.