Due to the interconnected characteristics between subsystems and the strong correlation within subsystems, the monitoring of plant-wide processes has become a challenging problem, especially for tandem plant-wide processes that exist in various industrial fields, such as petrochemicals, metallurgy, and sewage treatment. In this article, a novel spatio-temporal monitoring method is proposed for the hot strip mill (HSM) process, a typical tandem industrial process. First, the plant-wide process is divided into different subblocks based on the tandem structure. Then, a distributed conditional variational recurrent autoencoder-based process monitoring method is proposed to build the local latent variable model of each subsystem using relevant dynamic features extracted from the previous subsystem. The latent distributions and reconstructed errors are used to design local monitoring statistics for local process monitoring. A global monitoring statistic is established by deep support vector data description to monitor the whole process. Finally, the effectiveness and superiority of the proposed method are demonstrated by a HSM process case, which shows better monitoring performance compared to the existing methods.