Strategies to interpret short-wave infrared hyperspectral images of rocks involve the application of analysis and classification steps that guide the extraction of geological and mineralogical information with the ultimate aim of creating mineral maps. Previous interpretation strategies often rely on the use of statistical measures between reference and image spectra, which are scene dependent. Therefore, classification thresholds based on statistical measures to create mineral maps are also scene dependent, which is problematic because thresholds have to be adjusted between images to produce mineral maps of the same accuracy. We developed an innovative, knowledge-based interpretation strategy to perform mineralogical analyses and create classifications that overcome this limitation by using physics-based wavelength positions of absorption features, which are invariant between scenes, as the main sources of mineral information. The interpretation strategy is implemented using the open source Hyperspectral Python package (HypPy) and demonstrated on a series of laboratory-acquired hyperspectral images of hydrothermally altered rock samples. The results show how expert knowledge can be embedded into a standardized processing chain to develop reproducible mineral maps without relying on statistical matching criteria.