Procedural Content Generation of game content has been vastly improved over the last years and is more and more adopted also in the game industry. It relies mostly on evolutionary and related optimization methods but usually only treats a single of the many available facets as visuals, levels, audio, etc. The problem of how to combine several facets of generation is largely unsolved, but nevertheless very important. One of its subproblems is that we currently do not know in advance how users will react to machine-generated combinations. Based on a simple maze game with exchangeable visuals and audio styles we test how users receive ‘usual’ and ‘unusual’ facet compositions by means of rank trace based annotations of their own play-throughs. By means of machine learning techniques, we establish a model in order to learn and predict user reactions. Understanding the effects of facet composition on the user is fundamental if we want to rise evolutionary generation of content to the next level.