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Abstract: Stirred tank reactors are frequently used for mixing as well as heat- and mass transfer processes in chemical and biochemical engineering due to their robust operation and extensive experiences in the past. However, for cell culture processes like mammalian cell expression systems, special requirements have to be met to ensure optimal cell growth and product quality. One of the most important requirements to ensure ideal transport processes is a proper mixing performance, characterized typically by the global mixing time or the dimensionless global mixing time . As an evaluation method for mixing time determination, the time is usually determined until a tracer signal (e.g. conductivity) has reached a constant value after a peak has been introduced (e.g. by adding a salt). A disadvantage of this method is, that the position of tracer feeding as well as the position of the probe significantly influences the detected mixing time. Further on, the global mixing time does not provide any information about the spatial and temporal ”history” of the mixing process to identify areas that are mixed poorly or areas that form stable compartments. To overcome this disadvantage, a novel image analysis will be presented in this study for the detailed characterization of mixing processes by taking into account the history of mixing. The method is based on the experimental determination of the local mixing time distribution by using a multi-color change caused by a pH-change in a bromothymol blue solution. A 3 L transparent stirred tank reactor is used for the benchmark experiment. To demonstrate the suitability of the new characterization method for the validation of numerical simulations, a calculation with a commercial Lattice-Boltzmann approach (M-Star CFD) has been performed additionally and evaluated regarding mixing time distributions. The exemplary application of image analysis to a numerical mixing time simulation shows good agreement with the corresponding experiment. On the one hand, this shows that the method can also be interesting for numerical work, especially for experimental validation, and on the other hand, this allows much deeper insights into the mixing behavior compared to conventional mixing criteria. For example the new method enables the characterization of mixing on different scales as well as the identification of micro- and macroscopic flow structures. The strong influence of the acid to base ratio on mixing time experiments becomes clearly visible with the new method.