Spectral decomposition is a powerful interpretation tool that provides superior subsurface images of channels and other thinly bedded depositional systems. The analysis of the band-limited components is commonly facilitated through the red-green-blue (RGB) blend, which is limited to three volumes at a time, primarily selected based on the interpreter preference. Fortunately, machine learning technology provides the opportunity to quantitatively use many frequency volumes. We analyze twelve spectral magnitude components using multivariate feature selection techniques. The chosen subsets of features are used to classify seismic facies of a fluvial reservoir in the Malay Basin, offshore Malaysia. We find that the subset of spectral components gives a better classification result than the whole set. The sequential forward selector and the embedded selector of random forest algorithms provide the best subset of features that differentiate the desired classes. © 2022 Society of Exploration Geophysicists and the American Association of Petroleum Geologists.