In this report, the neural identification problem is outlined and the identifiability question for a general class of recurrent neural networks is addressed. As part of the intelligent flight control concept program, recurrent second-order neural networks are utilized in order to continuously identify critical stability and control parameters during flight. Our group at Washington University participated in Phase II, the online learning, with neural networks that learn new information during flight. In particular, a recurrent second-order neural network architecture with a robust filtered error learning algorithm was utilized to identify the dynamics of an F-15 aircraft. While the emphasis of our work has been on the development and implementation of online neural network estimators, we shall also include results with and without the baseline network. Several examples including in-flight situations are presented and the effectiveness of the recurrent high-order neural networks is illustrated.