A novel technique of on-line batch process monitoring based on the wavelet-based multi-hidden Markov model tree (MHMT) is developed. Unlike most of the existing batch process monitoring methods for only time scale, MHMT cannot only analyze the measurements at multiple scales in time and frequency but also capture the clustering and persistence of the statistical characteristics for practical measured data. This approach provides less signal distortion and better understanding of the principal source of the system variability affecting the process. In order to conduct the on-line batch monitoring, a simple modification of the unfolded structure that trains MHMT to set up the batch-monitoring model is derived. Also, the tying structure of MHMT for increasing the number of training data is developed. The proposed method is developed in this paper to improve the conventional MPCA-based method by extending the time-domain analysis into the time-frequency using the stochastic model analysis. After extracting the essential features of the past operating information, subsequently, two simple monitoring charts are presented to track the progress of each batch run and monitor the occurrence of observable upsets. The applications are discussed through two sets of benchmark data, a DuPont industrial batch polymerization reactor and a fed-batch penicillin production, those of which are characterized by some fault sources to illustrate the advantages of the proposed method in comparison to some conventional methods. (c) 2006 Elsevier Ltd. All rights reserved.