The Kalman and H_infin filters, which aim to minimize separate criteria, are optimal only in ideal circumstances. A question that arises in practice is how to determine which filter performs better using a posterior common criterion pertinent to the application. To address this issue, the authors use the mean squared error as a measure of performance while ensuring that the filters' tuning parameters are also comparable. Analysis of combined state variable and parameter estimation, in the area of vehicle dynamics, has shown that the H_infin filter has the ability to outperform the Kalman filter as long as the respective Riccati equations start from the same initial condition