Fault detection is necessary to assure safe operations of plant and productivity at desired level. Most of the industrial process data are highly correlated, which causes an issue with analyzing huge process data. To address this issue, the Partial least squares (PLS) method has been used successfully in process monitoring. However, measured process data are usually contaminated with errors that mask the important features in the data and reduce the effectiveness of any fault detection method in which these data are used. Unfortunately chemical process data (as in the case of most practical data) usually possess multiscale characteristics, meaning that they contain feattires and noise that occur at varying contributions over time and frequency. Wavelet-based multiscale representation of data has been shown to be a powerful data analysis, modeling, and feature extraction tool due to its ability to provide efficient separation of deterministic and stochastic features. Hence, the objective of this paper is to extend our previous work (Botre et al., 2016) to deal with autocorrelated and noise in the dataset with multiscale representation and present a new fault detection method of chemical processes using Multiscale Partial Least Square (MSPLS)-based generalized likelihood ratio test (GLRT) technique. The efficiency and robustness of the proposed method are demonstrated through two illustrative examples, one using simulated continuous stirred tank reactor (CSTR) and the other using Tennessee Eastman process problem (TEP) data. The results demonstrate the effectiveness of developed MSPLS-based GLRT over the conventional PLS-based techniques for fault detection in CSTR and TEP processes. (C) 2017 Elsevier Ltd. All rights reserved.