To address the asynchronicity issue of the multiple sensor data of the Jiangyin Bridge measured during a shipbridge collision, a statespace (SS) model was proposed to identify the time lag between the asynchronous accelerations at different locations of the bridge. First, one of the accelerations was randomly chosen as a reference signal, and the time axes of the rest of them (termed as time shifted signals) were individually shifted relatives to that of the reference signal with a series of time instant. Then, the SS model with two output variables, i.e., one reference signal and one time shifted signal, was formulated in correspondence with each time instant. The system matrices were computed by a datadriven stochastic subspace identification algorithm and the model order was estimated by the Akaike’s information theoretic criterion (AIC) and final prediction error (FPE). If the two accelerations for model fitting are asynchronous, errors may be introduced into the formulated SS model and its loss function is expected to be greater than the counterpart obtained with synchronous accelerations. Therefore, the actual time lag between them can be identified from the time instant that corresponds to the minimum of loss function. To evaluate the reproducibility of the SS model for time synchronization, asynchronous acceleration data measured two hours ahead of the shipbridge collision were analyzed as well. In addition, the synchronous acceleration data measured long after the shipbridge collision were utilized to examine its antifalseidentification capability. The results show that the SS model achieves a satisfactory performance in the identification of the time lag for both asynchronous and synchronous measurement data.