A sensorimotor control task often requires an accurate estimation of the timing of the arrival of an external target (e.g., when hitting a pitched ball). Conventional studies of human timing processes have ignored the stochastic features of target timing: e.g., the speed of the pitched ball is not generally constant, but is variable. Interestingly, based on Bayesian theory, it was recently shown that the human sensorimotor system achieves the optimal estimation by integrating sensory information with prior knowledge of the probabilistic structure of the target variation (Kording et al. 2004; Kording and Wolpert 2004). In this study, we tested whether Bayesian integration is also implemented while performing a coincidence-timing type of sensorimotor task by manipulating the trial-by-trial variability (i.e., the prior distribution) of the target timing. As a result, within several hundred trials of learning, subjects were able to generate systematic timing behavior according to the width of the prior distribution, as predicted by the optimal Bayesian model. Considering the previous studies demonstrating that the human sensorimotor system uses Bayesian integration in spacing and force-grading tasks, our result indicates that Bayesian integration is fundamental to all aspects of human sensorimotor control. Moreover, it was noteworthy that the subjects could adjust their behavior after the prior distribution was switched from wide to narrow or vice versa, but the adjustment was slower when the prior distribution was switched from wide to narrow. Based on a comparison with observations in a previous study, we discuss the flexibility and adaptability of Bayesian sensorimotor learning.