Abstract: In multi-stage press hardening, the product properties are determined by the thermo-mechanical history during the sequence of heat treatment and forming steps. To measure these properties and finally to control them by feedback, two soft sensors are developed in this work. The press hardening of 22MnB5 sheet material in a progressive die, where the material is first rapidly austenitized, then pre-cooled, stretch-formed, and finally die bent, serves as the framework for the development of these sensors. To provide feedback on the temporal and spatial temperature distribution, a soft sensor based on a model derived from the Dynamic mode decomposition (DMD) is presented. The model is extended to a parametric DMD and combined with a Kalman filter to estimate the temperature (-distribution) as a function of all process-relevant control variables. The soft sensor can estimate the temperature distribution based on local thermocouple measurements with an error of less than 10 °C during the process-relevant time steps. For the online prediction of the final microstructure, an artificial neural network (ANN)-based microstructure soft sensor is developed. As part of this, a transferable framework for deriving input parameters for the ANN based on the process route in multi-stage press hardening is presented, along with a method for developing a training database using a 1-element model implemented with LS-Dyna and utilizing the material model Mat248 (PHS_BMW). The developed ANN-based microstructure soft sensor can predict the final microstructure for specific regions of the formed and hardened sheet in a time span of far less than 1 s with a maximum deviation of a phase fraction of 1.8 % to a reference simulation.