Digital Photography has caused an exponential increase of captured images. The process of reviewing and selecting the most beautiful images, in regard to some well-known aesthetic criteria, is a time consuming task. Excellent and repeatable face and object-recognition and image classification tasks of various kinds of images (portrait, landscape, macro, sports) are the result of applied machine learning algorithms. Because of the increase in hardware computing-power deep neural networks are becoming more and more popular. This master thesis analyses solutions and tools which can (semi)automatically find the most aesthetically pleasing images from a sequence of images. In this thesis the methods and tools of classical machine learning and those of deep convolutional neural networks are compared. On the basis of my professional photographic experience and images in various fields of photography (documentary, wedding, sport) a comercial prototype application is tested and some solutions to the problem are suggested.