The diploma thesis presents the entire process of developing a solution for recognising person’s age in the image. We start with the theoretical basics of convolutional neural networks that we used to address the problem. In the practical part we start with the preparation of used datasets and continue with learning of our neural network with the chosen widely known VGG architecture. Learned model is tested on the LAP competition dataset in order to obtain results, which are comparable with the solutions of other teams. Despite somewhat simpler approach our results proved to be quite encouraging. We surpassed human performance and ranked 4th among 11 teams.