Hand position and gesture recognition from an image stream is a topic of relevance for developing human-machine interactions. The advent of low-cost cameras in the consumer market, like Microsoft Kinect, leaves open the possibility of build recognition applications, which are not affected by low light conditions. This paper is a survey of the literature on hand position and gesture recognition with the use of depth cameras. It is noticeable that reviewed papers focus on the recognition of one-handed gestures and their classification among a finite set of gestures. Last year’s advances in hand processing include techniques for posture recognition without restrictions, but it is unknown their effectiveness on low-cost hardware because testing were done without a standardized set of images and with a diversity of hardware.