In this paper, a new dynamic kernel partial least squares (D-KPLS) modeling approach and corresponding process monitoring method are proposed. The contributions are as follows: (1) Different from standard kernel partial least squares, which performs an oblique decomposition on measurement space. D-KPLS performs an orthogonal decomposition on measurement space, which separates measurement space into quality-related part and quality-unrelated part. (2) Compared with the standard KPLS algorithm, the new KPLS algorithm, D-KPLS, builds a dynamic relationship between measurements and quality indices. (3) By introducing the forgetting factor to the model, i.e., the samples gathered at the different history time are assigned to different weights, so the D-KPLS model builds a more robust relationship between input and output variables than standard KPLS model. On the basis of proposed D-KPLS algorithm, corresponding process monitoring and quality prediction methods are proposed. The D-KPLS monitoring method is used to monitor the numerical example and Tennessee Eastman (TE) process, and faults are detected accurately by the proposed D-KPLS model. The case studies show the effeteness of the proposed approach. (C) 2016 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.