Autonomous robots are critical components of factories of futures. In this era, autonomous transfer vehicles are expected to play important role for flexible manufacturing. But the system should detect abnormal events itself. In this study, anomaly detection approach is proposed for autonomous transfer vehicles in the smart factories. Decision trees are used to detect stopping and slow down anomalies in internal transportation of the factories. The proposed approach is tested in simulation environment.