A statistical pattern recognition technique based on time series analysis of vibration data is presented in this paper. A 20-m riser model experimentally validated is used for the numerical implementation of this technique. The dynamic response of the riser model is assessed using a semi-empirical approach with an increased mean drag coefficient model during lock-in events. Because structural damage is associated with fatigue damage, hinge connections are used to represent several damage scenarios. Then, the statistical pattern recognition technique is used to identify and locate structural damage using vibration data collected from strategically located sensors. Sensor locations are obtained from an optimum sensor placement method. The numerical results show that structural degradation due to fatigue in oscillating flexible risers can be assessed using the presented statistical pattern recognition technique.