This chapter presents a new promising technique for design of inferential sensors in chemical process industry which has a broad range of applicability. It is based on the concept of evolving fuzzy rule-based systems (EFS). Mathematical modeling was used to infer difficult to measure otherwise variables such as product quality since 1980s, including in on-line mode. The challenge today is to develop such adaptive, flexible, self-calibrating on-line inferential sensors that reduce the maintenance costs while keeping high precision and interpretability/transparency. The methodology of fuzzy rule-based models of Takagi-Sugeno type (Takagi-Sugeno, 1985) which have flexible, open structure and are therefore called ‘evolving’ (see also chapter 2) is particularly suitable for addressing this challenge. The basic concept is described from the point of view of the implementation of this technique to self-maintaining and self-calibrating inferential sensors for several chemical industry processes. The sensitivity analysis (input variables selection) was performed on-line and this was compared to the off-line input variables selection using genetic programming (GP). A case study based on four different inferential sensors for estimating chemical properties is presented in more detail, while the methodology and conclusions are valid for the broader area of chemical and process industry in general. The results demonstrate that well interpretable and with simple structure inferential sensors can be designed automatically from the data stream in real-time that provide estimation of the real values of process variables of interest. The proposed approach can be used as a basis for development of a new generation of inferential sensors that can address the challenges of the modern advanced process industry. (c) IEEE Press and John Wiley and Sons