Response styles represent a challenge for empirical survey research. Response behavior which is not associated with the content of a survey question can not only influence proportional and average values but also distort model-based parameter estimation. In this pilot study we investigate whether machine learning (ML) methods can be used to construct an empirically based approach to a uniform identification of the most common response style patterns. This method could replace more complex control procedures, which can only find some patterns or are associated with considerable additional effort. Our approach could, therefore, be a more universal and practical option. The method presented here is based on the calibration of the ML model using synthesized data that correspond to a formal definition of response styles (RS) and a proportion of empirical data (European Social Survey) which is not affected by RS. The trained model can then be applied to empirically collected data in order to reliably detect and process RS patterns in survey data. With error classification rates of 0.3 to 3.5 %, the results of this study suggest that our ML-based response style detection is a promising alternative to previous methods.