In this paper, we propose the use of non-negative matrix factorization (NMF) of multivariate spectra for plantwide oscillation detection. One of the key features of NMF is that it provides a parts-based representation that allows us to retain the causal basis spectral shapes or parts that constitute the spectra of measurements, unlike the popular principal component analysis (PCA)-based methods. The contributions of this paper are as follows: (i) a novel measure known as the pseudo-singular value (PSV) to assess the order of the basis space (the PSV is also useful in determining the most dominant features of a data set); (ii) a power decomposition plot that contains the total power (defined in this work) and its decomposition by NMF (the power plot is a useful and compact visual tool that provides overall spectral characteristics of the plant and shows the decomposition of these characteristics into well-localized frequency components); and (iii) a novel measure defined as the strength factor (SF) to assess the strength of the localized features in the variables (it can be also used in isolating the root cause). Finally, it is shown that the proposed implementation of NMF is powerful and sensitive enough to capture small oscillations in the measurements. As a result, it largely eliminates the need to filter the data. Industrial case studies are presented to illustrate the applications of NMF and to demonstrate the utility and practicality of the proposed measures.