Wire ropes are exposed to huge external powers every day. Unfortunately, this can lead to structural anomalies or even defects in the rope formation. A defective rope bears a high risk for human life. This motivates the strict rules summarized in the European norm [1], which instruct a regular inspection of wire ropes. Rec ent approaches to defect detection in surfaces or fabrics mostly are limited to texture analysis [2]. Usually not enough defective samples are available in a dvance. Therefore texture-based approaches are often combined with oneclass classification, also called novelty detection [3]. In [4] anomaly detection in wir e rope data was performed using linear prediction as feature extraction combined with a multi-channel one-class classification strategy. However, all these approaches are not able to detect structural changes in the rope structure. Thus, we propose a new approach for model-based detection of surface defects and structural deviations from the normal rope geometry. A wire rope is composed of strands which itself are composed out of single wires. Every wire can be described by a helix and a mathematical description of the rope geometry can be given by the space curve of the j-th wire in the i-th strand: @