To obtain an identified model from data, the system identification practitioner has to make an important choice: to specify the set of candidate models, or model structure. This choice can play an outsized role on the success or failure of the identification process. If the model structure is specified too restrictively, so that the true system is not represented, then the identified model will be biased. On the other hand, if the model structure is specified too generally, then the identified model can have a high variance, and a significant amount of data may be needed to reduce the sensitivity to measurement noise. Ideally, the practitioner should choose the model structure so that it encodes all the information that is known with high confidence. The structured nonlinear system identification approach is designed to give the practitioner a very flexible model structure that can easily be configured to be as restrictive or permissive as the a-priori information about the system warrants. In this chapter, a complete introduction to structured identification is developed, with examples relevant to many different real-world applications integrated throughout.