The purpose of this study was to utilize NIR spectrometry to develop a novel method to detect and determine concentrations of different soils in dishwashing liquor during automatic dishwashing in real-time. If it is possible to differentiate between soils, this could be an opportunity to react specifically to them (e.g. by increasing the water temperature if fat components are not sufficiently emulsifying). The possibility of an automatic adaptation of the dishwashing process to different soils and soil levels could lead to a shorter, more environmentally friendly and cost-reducing process. In a first approach, an emulsion containing three soil types (oatmeal, egg-yolk and butterfat), water and detergent were used to develop NIR spectrometry prediction models. Transmittance spectra obtained with an Fourier transform near infrared (FT-NIR) spectrometer of testing standards of 76 automatic dishwashing cycles with seven samples per cycle were taken at various times during the main washing process for calibration (and validation) of the NIR spectrometry prediction models. The spectra were pretreated to develop NIR spectrometry prediction models for each type of soil using the partial least squares regression method with cross-validation. Overall, the coefficients of determination in cross-validation are R² > 0.92 for all NIR spectrometry prediction models developed. The results of the prediction models developed show that NIR spectrometry technology is a promising method to predict different levels of predefined soils in dishwashing liquor. The NIR spectrometry models were applied to an automatic dishwashing process with soiled dishes instead of emulsions containing soils to test their applicability. The resulting dishwashing process could be tracked in real-time by the dissolved soil concentrations, observed in the dishwashing liquor.