System identification is about the theory and the practice for building mathematical models of dynamic systems from experimental data. This topic is common in many areas of sciences and technologies, though the term of “system identification” is usually used in the field of automatic control, where it is studied with the particularity of control systems. As a matter of fact, system identification plays an important role in the design of modern control systems, as most efficient controllers are model-based. The main idea of block-oriented nonlinear system identification is to model a complex system with interconnected simple blocks. Such models can cover a large number of industrial applications, and are yet simple enough for theoretic studies. It is therefore a good trade-off between the studies on general nonlinear systems producing few practically useful results, and those on specific nonlinear systems with a limited application scope. The objectives of the proposed project are to extend block-oriented nonlinear models with hysteresis blocks and bilinear blocks, and to relax some traditional restrictions on nonlinearity structures and on experimental conditions. The two extensions with hysteresis blocks and bilinear blocks have been motivated by their importance in process control. Through these extensions, it is expected to considerably increase the applicability of block-oriented nonlinear system identification to industrial systems, while contributing significantly to the progress of the researches on nonlinear system identification. Two case studies are planned in this project, namely, a control valve with stiction, and a fuel cell system, both with direct industrial backgrounds. These two case studies will not only serve as laboratory validations of the produced results, but also demonstrate their feasibility in industrial applications. The outcome of this project is expected to cover more industrial applications with the extended block-oriented models and to better adapt the identification methods to industrial application environments. By means of increasing the efficiency and the reliability of model-based control and monitoring systems, the benefits of this project may impact industrial production quality, energy and raw material saving, human and equipment safety, and environmental protection.