Steam Boilers are important equipment in power plants and the boilers trip may lead to the entire plant shutdown. To maintain performance in normal and safe operation conditions, detecting of the possible boiler trips in critical time is crucial. Artificial Neural network applications for steam boilers trips are developed designed and parameterized. In this present study, the developed systems are a fault detection and diagnosis neural network model. Some priori knowledge of the demands in network topology for specific application cases is required by this approach, so that the infinite search space of the problem is limited to a reasonable degree. Both one-hidden-layer and two-hidden-layers network architectures are explored using neural network with trial and error approach. 32 Boiler parameters are identified for the boiler FDDNN analysis. The power plant experience has been imposed to select the most important parameters related to the superheated monitoring contribution on the boiler trip.