Kernel Principal Component Analysis (KPCA) is an efficient multivariate statistical technique used for nonlinear process monitoring. Nevertheless, the conventional KPCA suffers high computational complexity in dealing with large samples. In this paper, a new kernel method based on a novel reduced Rank-KPCA is developed to make up for the drawbacks of KPCA. The basic idea of the proposed novel approach consists at first to construct a reduced Rank-KPCA model that describes properly the system behavior in normal operating conditions from a large amount of training data and after that to monitor the system on-line. The principle of the proposed Reduced Rank-KPCA is to eliminate the dependencies of variables in the feature space and to retain a reduced data from the original one. The proposed monitoring method is applied to fault detection in a numerical example, Continuous Stirred Tank Reactor and air quality-monitoring network AIRLOR and is compared with conventional KPCA and Moving Window KPCA methods.