The problem of gesture recognition is a very interesting problem because of the temporal dimension that exists and has to be handled somehow. There are several ways in which the problem can be solved and in this paper way that uses fully connected feed-forward neural network has been explored. To make this possible, it is necessary to transform the problem so it does not depend on the temporal dimension. Therefore, a few ways of sampling have been explored in order to eliminate the temporal dimension. Knowledge about the problem is implicitly stored in the weights between the neurons in the neural network. In order for neural network to better perform its job, it is necessary to better adjust the weights and that process is called learning. Two learning algorithms have been implemented, backpropagation algorithm and rprop algorithm. As in humans, much more time is spent on learning than on recognition itself. Because of that, methods that speed up learning had to be explored and one of those method is parallelization. With its architecture, GPU is an ideal candidate for parallel neural network learning. As part of this study, parallel processing at the network level have been explored, ie. one work item presents one instance of neural network. The problem of gesture recognition is still uncharted territory which is becoming more and more interesting, especially with the growing popularity of smart phones and touch screens. There are still a lot of things that need to be explored, like other learning algorithms or other architectures of neural networks.