Natural hazards caused by insect outbreaks, such as those induced by the European bark beetle (Ips typographus L.), are among the most extensive disturbances affecting forest health in various geographical regions. Accurate and up-to-date knowledge of the spatial distribution of bark beetle-infested trees is critical for forest managers to effectively plan appropriate countermeasures and predict future bark beetle infestation dynamics. In this study, three scenarios for mapping bark beetle-induced tree mortality based on airborne hyperspectral data (HyMap) were examined including combining a genetic algorithm (GA) for feature selection with a supervised support vector machine (SVM) classification. The scenarios differed in how the mortality classes (used to extract the spectral signatures) were defined. Scenario 1 included three mortality classes, while Scenario 2 and 3 each included only one general mortality-related class but with different definitions. The classes, derived from the three scenarios, served as inputs into feature selection facilitated by the GA. The most stable bands, selected by the GA, were then used as input bands for a bootstrapped supervised SVM classification procedure. In two of the three scenarios, the trained classifier was additionally applied to a second independent hyperspectral image to evaluate the stability of the spectral signatures derived from the training samples. The classification procedure was further refined by varying the number of input bands and the input data types, where the bands not only included the original reflectances of the HyMap images but also a first order Savitzky–Golay derivation and continuum-removed spectra.