The bridge monitoring system consists of a large amount of sensors,and there are likely to be some similarities among the data collected by different sensors.To figure out the similarity relations,a pattern-shape distance based similarity measurement approach was proposed.Firstly,the monitoring time series was divided into several patterns according to the morphological characteristics.Secondly,similarity discrimination based on the difference of dynamic change trend of each pattern shape was discussed,and the distance function of each discrimination result was defined.Thus,the pattern shape distance of different monitoring points could be calculated.On this basis,similarity analysis of monitoring points on Yufeng Bridge was discussed by means of hierarchy clustering.The clustering result shows good consistence with bridge real states.The similarity analysis of monitoring points provides the potential of deeper information mining of bridge structures and scientific basis in sensor troubleshooting and discrimination of abnormal data.