The partial least-squares (PLS) method is widely used in the quality monitoring of process control systems, but it has poor monitoring capability in some locally strong nonlinear systems. To enhance the monitoring ability of such nonlinear systems, a novel statistical model based on global plus local projection to latent structures (GPLPLS) is proposed. First, the characteristics and nature of global and local partial least-squares (QGLPLS) are carefully analyzed, where its principal components preserve the local structural information in their respective data sheets as much as possible but not the correlation. The GPLPLS model, however, pays more attention to the correlation of extracted principal components. GPLPLS has the ability to extract the maximum linear correlation information; at the same time, the local nonlinear structural correlation information between the process and quality variables is extracted as much as possible. Then, the corresponding quality-relevance monitoring strategy is established. Finally, the validity and effectiveness of the GPLPLS-based statistical model are illustrated through the Tennessee Eastman process simulation platform. The experimental results demonstrate that the proposed model can maintain the local properties of the original data as much as possible and yield monitoring results that are better than those of PLS and QGLPLS.