This book discusses the popular topic: fault detection and isolation (FDI) with introductory literature surveys, comparison tests and simulations performed on a UAV model. FDI is applied both in academia and industry resulting in many publications over the past 50 years or so. However few publications consider neural networks in comparison to traditional techniques such as observer based, parameter estimation and parity space approaches. The first part of this book serves as an introduction to FDI and attempts to summarise all existing approaches. The remaining chapters consider a neural network application to a UAV simulation with both single and multiple sensor faults considered. Furthermore a comparison to the popular Extended Kalman Filter is demonstrated. In conclusion this book can serve as an introduction to FDI, demonstration of neural network-based FDI, and comparison to the traditional nonlinear EKF.