In modern complex systems and machines - e.g., automobiles or construction vehicles - different versions of a "Condition Based Service" (CBS) are deployed for maintenance and supervision. According to the current state of the art, CBS is focusing on monitoring of static factors and rules. In the area of agricultural machines, these are for example operating hours, kilometers driven or the number of engine starts. The decision to substitute hydraulic oil is determined on the basis of the factors listed. A data-driven procedure is proposed instead to leverage the decision-making process. Thus, this paper presents a method to support continuous oil monitoring with the emphasis on artificial intelligence using real-world spectral oil-data. The reconstruction of the spectral data is essential, as a complete spectral analysis for the ultraviolet and visible range is not available. Instead, a possibility of reconstruction by sparse supporting wavelengths through neural networks is proposed and benchmarked by standard interpolation methods. Furthermore, a classification via a feed-forward neural network with the conjunction of Dynamic Time Warping (DTW) algorithm for the production of labeled data was developed. Conclusively, the extent to which changes in hyper-parameters (number of hidden layers, number of neurons, weight initialization) affect the accuracy of the classification results have been investigated
