Model-based monitoring systems based on state observer theory often have poor performance with respect to accuracy, bandwidth, reliability (false alarms), and robustness. Previous works have investigated quantitatively the above limitations from the machine monitoring viewpoint and have developed a design methodology for discrete-time well-conditioned state observers. In this paper, the estimation performance of well-conditioned observers is demonstrated on a DC spindle system designed and built for this purpose. The results show that the robustness of the estimate is similar to that obtained with the well-known Kalman filtering technique. Additional simulation-based examples show that the transient as well as steady-state error robustness to perturbations is better than or equal to Kalman filter performance depending on the nature of the modeling error. Because of this robustness, the well-conditioned observer for discrete-time systems is an important technique for the development of improved machine monitoring systems.