When wind turbine driving system (WTDS) undergoes abnormal conditions, the fault information hid-den in WTDS scatters over multiple signal channels and hence inadequate for fault diagnosis only via fault information extraction of single-channel signal. To make full use of multichannel fault information of WTDS and improve diagnostic accuracy, this paper proposes a new approach based on multivariate singular spectrum decomposition (MSSD) and improved Kolmogorov complexity (IKC). Firstly, based on singular spectrum decomposition (SSD) and the idea of multichannel data processing, a multivariate singular spectrum decomposition (MSSD) method is presented to process multichannel vibration data collected from WTDS, which can obtain adaptively multichannel mode components without extra user-defined parameters. Secondly, through incorporating symbolization process into Kolmogorov complexity (KC), an improved complexity metric abbreviated as IKC is proposed to capture the fault information of multichannel mode components, which can enhance fault feature extraction ability of KC. Finally, IKC-based multichannel fault features are fed into partial least squares (PLS) to automatically discriminate different fault patterns of WTDS. Practical engineering data from WTDS demonstrate the effectiveness of the proposed approach. Additionally, the superiority of the proposed approach has also proven in extracting fault information and health condition identification compared to the other multichannel methods and traditional single-channel approaches reported in the literature. (c) 2021 Elsevier Ltd. All rights reserved.