The dynamic time-varying characteristic has brought great challenges to the plant-wide process monitoring. In this article, a distributed adaptive principal component regression algorithm is proposed for the online indicator monitoring of large-scale dynamic process. First, the distributed data subblocks are constructed according to the process operation units. In each subblock, an adaptive resampling method based on the subblock data and plant-wide data is presented to construct the modeling sample sets, which can extract the process local and global information simultaneously. Afterwards, the indicator-related feature is extracted, and the Bayesian method is used to integrate the subblock monitoring results. Through the collaborative monitoring of the process local and global feature spaces, a refined monitoring decision can be obtained. Finally, a numerical example and Tennessee Eastman process are used to illustrate the effectiveness of the proposed method.