One of the significant challenges in the upper-limb-prosthetics research field is to identify appropriate interfaces that utilize the full potential of current state-of-the-art neuroprostheses. As the new generation of such prostheses paces towards approximating the human physiological performance in terms of movement dexterity and sensory feedback, it is clear that current non-invasive interfaces are still severely limited. Surface electromyography, the interface ubiquitously used in the field, is riddled with several shortcomings. Gesture recognition, an interface pervasively used in wearables and mobile devices, shows a strong potential as a non-invasive upper-limb prosthetic interface. This study aims at showcasing its potential in the field by using gyroscope sensors. To this end, we (1) explore the viability of Dynamic Time Warping as a classification method for upper-limb prosthetics and (2) look for appropriate sensor locations on the body. Results indicate an optimal classification rate of 97.53%, σ = 8.74 using a sensor located proximal to the endpoint performing a gesture.