The problem of state reconstruction in dynamical systems, known as observer problem, is undoubtedly crucial for controlling or just monitoring processes. For linear systems, the corresponding theory has been quite well established for several years now, and the purpose of the present book is to propose an overview on possible tools in that respect for nonlinear systems. Basic observability notions and observer structures are first recalled, together with ingredients for advanced designs on this basis. A special attention is then paid to the well-known high gain techniques with a summary of various corresponding recent results. A focus on the celebrated Extended Kalman filter is also given, in the perspectives of both nonlinear filtering and high gain observers, leading to so-called adaptive-gain observers. The more specific immersion approach for observer design is then emphasized, while optimization-based methods are also presented as an alternative to analytic observers. Various practical application examples are included in those discussions, and some fields of application are further considered: first the problem of nonlinear output regulation is reformulated in a perspective of observers, and then the problem of parameter or fault estimation is briefly mentioned through some adaptive observer tools.