A tracking simulator is an online simulation system that achieves a permanent state synchronization with the targeted process by dynamically calibrating the model state after comparing process measurements with model results. Tracking simulators are a powerful industrial application that can be utilized as a plant-wide virtual sensor for process monitoring and diagnosis as well as a predictive tool to provide production forecasts based on the current state of the plant. In a tracking simulator, the online calibration is performed by a dynamic estimation method. One of the most adopted dynamic estimaton methods is implicit dynamic feedback, which is based on the adjustment of model parameter using feedback controllers to align simulation results and process outputs. Thus far, PI controllers have been the most popular approach for the implementation of implicit dynamic feedback estimators. Other feedback control techniques could be employed to improve the reliability of applications based on this estimation method. This paper presents an implicit dynamic feedback estimation approach based on sliding mode controllers (SMC) for industrial tracking simulation systems. In contrast to PI controllers, SMC controllers can be more easily tuned and they are robust against uncertainties related to the simulation model behavior and measurement noise. In this work, the SMC-based approach for estimation of tracking simulation systems is described, implemented and tested using a representative laboratory-scale process.