This masters thesis presents Autofeature, a novel algorithm that enables the automatic extraction of material specific spectroscopic characteristics from an annotated infrared spectroscopy dataset. With these characteristics the material can then be identified in hyperspectral images. Accordingly, no expertise of the user in the spectroscopic properties of the material is necessary. On the one hand, the AutoFeature algorithm generates thousands of features based on template matching and on the other hand, selects the most promising features based on statistical and machine learning methods. Four types of templates are designed: triangles, Gaussian bells, general Gaussian bells and straight lines. The matching is performed at all possible infrared spectrum positions by employing the Pearson correlation coefficient. The subsequent feature selection is carried out with fast function extraction, embedded random forest modelling or with one of the following three filter selection methods ReliefF, Fisher score and HSIC lasso. The study first investigates the properties of the AutoFeature algorithm concerning sample size and noise. Next, features are automatically extracted from three real-world data sets containing microplastic and skin tissue specimens. These features are then used to train random forest classification models for class predictions of five polymers in the first experiment, melanoma and non-melanoma in the second experiment, and connective tissue and non-connective tissue in the third experiment. For artificial data, the algorithm was able to extract correct features for noise levels of 10% for a sample size of 16 respectively 25% for sample size 100. For real-world data, features of all four types are extracted and the features are only located at characteristic absorption bands of the substances being investigated. The exact positions and widths of some features are unexpected though. The validation of the random forest models with unseen test data yielded classification accuracies of 99.6% or higher for the polymer predictions and a perfect classification for the melanoma and connective tissue predictions. While the different selection methods result in features with different probability density functions, they all yield features with convincing class discrimination properties. Overall, the AutoFeature algorithm was able to automatically extract features that were chemically meaningful and suited for prediction tasks for both artificial and real-world data. To evaluate further potential of the algorithm, examinations with datasets of greater variety need to be performed. We believe, by designing additionaltemplates and adapting parameters of the selection methods, further algorithmic progress can be made.