A process monitoring method based on double-model and multi-subspace vine copula (DMVC) is proposed in this paper. To improve the fault detection performance, process variables are divided into two sets according to their correlation, in which C-vine and D-vine are used to build models, respectively. The variables with stronger correlation are selected to build the C-vine model, and those with weak correlation are used for the D-vine model. In addition, the two models individually establish three different subspaces according to different training data information, including marginal distribution subspace (MDS), dependent structural subspace (DSS), and joint distribution subspace (JDS). Highest density region (HDR) and generalized local probability (GLP) are also introduced to establish a robust control domain and detection index, which makes the method more sensitive to data. The effectiveness of the proposed method in the field of process monitoring is verified by numerical simulation and the Tennessee Eastman (TE) process. Compared with classical multivariate statistical methods, the DMVC displays superior monitoring performance.