**Abstract** This bachelor-thesis analyses the motion sequence of industrial robots for recurring cycles, which are generated by tasks. The goal of this bachelor-thesis is to indentify recurring cycles und find groups of cycles, which contain a high waiting time deviation. For this purpose the motion sequence of the robot is filtered, to ignore long downtimes for the analysis. Subsequent the recurring cycles are extracted from the movement data. Resulting cycles, that are very similar, are grouped at a similarity of 85%. The standard deviation of the standard deviations of every waiting time sequence is used to analyse the waiting times in clusters. Frequent occurring clusters, which have a high standard deviation of the standard deviations, are critical for the waiting time deviation. Clusters, that occure more than two times and have a standard deviation greater than 3.5 are marked as critical. Critical segments in critical clusters are identified by the standard deviation of the waiting times in each segment in critical clusters. Critical segments are segments, which standard deviation of the waiting times is greater than the mean value of the standard deviations of all segments in a cluster. With discovery of critical segments, the line and module of the robotprogramm on the specific segment are allocatable. Furthermore the average length of a task can be calculated. Afterwards the correlation of the clusters is reviewed. Here an inspection of the preceding cluster of critical clusters is made. At the end, the introduced method is verified by example data. In the absence of tagged measurement data, the waiting time deviation gets simulated. The simulation shows, that critical segments are identified by the introduced method.