Abstract: The use of statistical process control (SPC) in monitoring and diagnosis of process 
and product quality profiles remains an important problem in various manufacturing 
industries. The SPC problem with a nonlinear profile is particularly challenging. 
This paper proposes a novel scheme to monitor changes in both the regression 
relationship and the variation of the profile on-line. It integrates the multivarivate 
exponentially weighted moving average procedure with the generalized likelihood ratio test
 based on nonparametric regression. The proposed scheme not only provides an effective SPC
 solution to handle nonlinear profiles, which are common in industrial practice, but it 
also resolves the latent problem in popular parametric monitoring methods of being unable 
to detect certain types of changes due to a misspecified, out-of-control model. Our 
simulation results demonstrate the effectiveness and efficiency of the proposed monitoring
scheme. In addition, a systematic diagnostic approach is provided to locate the change 
point of the process and to identify the type of change in the profile. Finally, a deep 
reactive ion etching example from semiconductor manufacturing is used to illustrate 
the implementation of the proposed monitoring and diagnostic approach.