Today, sports and activity trackers are ubiquitous. Especially runners and cyclists have a variety of possibilities to record and analyze their workouts. In contrast, climbing did not find much attention in consumer electronics and human-computer interaction. If quantified data similar to cycling or running data were available for climbing, several applications would be possible, ranging from simple training diaries to virtual coaches or usage analytics for gym operators. This paper introduces a system that automatically recognizes climbed routes using wrist-worn inertia measurement units (IMUs). This is achieved by extracting features of a recorded ascent and use them as training data for the recognition system. To verify the recognition system, cross-validation methods were applied to a set of ascent recordings that were assessed during a user study with eight climbers in a local climbing gym. The evaluation resulted in a high recognition rate, thus proving that our approach is possible and operational.