Da ta from most industrial processes contain contributions at multiple scales in t ime and frequency. In contrast, most existing methods for fault detection are best for detecting events at only one scale. This paper provides experimental validation and insight into a new method of process fault detection based on the integration of multiscale signal representat ion and scale-specific clustering-based diagnosis. The multiscale ART-2 (MSART-2) algorithm models normal process operation as clusters of wavelet coefficients at different scales. It detects a process change when one or more wavelet coefficients of test da ta violate similarity thresholds with respect to clusters of normal da ta at tha t scale. Especially in industrial situations where the nature of the abnormal features is not known a priori, MSART provides bet ter average performance due to its ability to adapt to the scale of the features. In contrast to most other multiresolution schemes, this framework exploits clustering behavior of wavelet coefficients of multiple variables for the purpose of scale selection and feature extraction. Detailed performance comparisons, based on rigorous MonteCarlo simulations as well as industrial da ta from a large scale petrochemical process, are provided. Our results show tha t MSART-2 significantly improves the detection performance of the ART-2 detection algorithm over a broad range of process anomalies. Results are compared with single-scale and multiscale versions of PCA for benchmarking purposes.