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This thesis concerns the design and development of neural network based predictive algorithms to predict approaching aircraft limits. Therefore, approximate dynamics of flight envelope parameters such as angle of attack and load factor are constructed using neural network augmented dynamic models. Then, constructed models are used to predict steady state responses. By inverting the models and solving for critical controls at the known envelope limits, critical control inputs are calculated as well. The performance of the predictor algorithm is then evaluated with a different neural network online adaptation law which uses a stack of recorded data. It is shown that using a stack of recorded data online, constructed models become much more representative of limit parameter dynamics compared to adaptation using instantaneous measured data only. The benefits of recording data online and using it for weight adaptation are presented in the scope of dynamic trim and control limit predictions.