High product quality and consistent operation safety are important to industry processes, particularly to those with a large number of correlated process variables. Principal component analysis (PCA) has been widely used in multivariate process monitoring for its ability in reducing the process dimension. PCA and other statistical techniques, however, have difficulties in differentiating the faults with similar time-domain process characteristics. A wavelet-based time-frequency approach is developed in this paper to improve PCA-based methods by extending the time-domain process features into time-frequency information. Subsequently, a similarity measure is presented to compare process features for on-line process monitoring and fault diagnosis. xperimental results show that the proposed multivariate time-frequency process feature is effective in both fault detection and diagnosis, illustrating the potentials for real world applications.