This chapter provides a practical overview of state and parameter estimation approaches for vehicle dynamics applications. In this chapter we will discuss various aspects which should be considered when setting up estimators to extract variables which are difficult to measure, or for which multiple (indirect) measurements are available in a sensor fusion context. In order to introduce this material we employ concrete Matlab examples on a real vehicle dataset, which should enable a concrete practical insight in the different methodologies. This chapter tackles methods ranging from regular least-squares data driven methods, to Kalman filtering for state and coupled state-input/parameter estimation. We present various basic vehicle methods which can be employed for various estimation problems. Finally, we also show the potential benefits of using batch based processing to further robustify estimation, at the expense of larger latency on the final results. This chapter should give the readers concrete insights which they can use to start developing estimation in vehicle dynamics applications, and beyond.