This paper presents a new approach to the identification of pH processes based on the Wiener model structure (a dynamic linear element in series with a static nonlinearity). A frequency sampling filter model is used to represent the dynamic linear element and a simple least-squares algorithm is applied to simultaneously estimate the parameters of the linear subsystem and the inverse static nonlinearity. Experimental results obtained from a pilot-scale pH process are presented to illustrate the performance of this new scheme. A key benefit of using this approach is that an estimate of the pH titration curve can be obtained in significantly less time as compared to more traditional approaches.