Recently, the unscented Kalman filter (UKF) algorithm, which is a new generalization of the Kalman filter for nonlinear systems, was proposed in the literature. It has significant advantages over its widely used predecessor, the extended Kalman filter (EKF). These include better accuracy and simpler implementation and the dispensability of system and measurement model differentiability. In this work, we compare the performance of the two approaches in a simulated pH process with three situations considered. The first one evaluates the performance differences between the unscented transform and the EKF linearization, as applied to the nonlinear pH output equation. In the second simulation, the complete filter algorithms are compared in a state estimation framework. The third case concerns parameter estimation. In all three cases, the UKF produced more-accurate results.