Computer vision systems are commonly used to design touch-less human-computer interfaces (HCI) based on dynamic hand gesture recognition (HGR), which have a wide range of applications in several domains, such as, gaming, multimedia, automotive, home automation. However, automatic HGR is still a challenging task, mostly because of the diversity in how people perform the gestures. In addition, the number of publicly available hand gesture datasets is scarce and often the gestures are not acquired with sufficient image quality, and the gestures are not correctly performed.For these reasons, we propose a dataset of 27 dynamic hand gesture types acquired at full HD resolution from 21 different subjects, which were carefully instructed before performing the gestures and were observed during the gesture performing; in case the performed hand gesture was not correct, the subjects had to repeat the movement. Each subject performed 3 times the 27 hand gestures for a total of 1701 videos collected and corresponding 204120 video frames.