Abstract : Emulating of electronic devices and circuits is an essential matter for the analysis and design of the analog/digital circuits. The accuracy of the emulating processes depends mainly on the accuracy of the models for the semiconductor devices forming the electronic circuits. One of these electronic devices is the MOSFET transistor that represents the most popular electronic device used in the analog and/ or digital circuits, and for this reason it has been chosen in this work. There are large number of models for the MOSFET ranged between the analytical models and the fitting models. In this project, an emulating model for the MOSFET transistor has been developed using neural networks. The model uses the external behavior (characteristics) of the MOSFET as a base for the model development. The training algorithm used during the model development is the ackpropagation training algorithm. The data used for the training process are taken from different sources, these are: 1. Data generated by the analytical existing MOSFET models. 2. Data generated from real and practical results for transistors with different dimensions fabricated using 0.35 µm technology. The results obtained using the neural networks based model when it compared with that real one, reflect the accuracy of the developed model. Moreover, the developed model shows an excellent response over different regions of operation for the transistor and under different biasing conditions. This in turn, reflects a fact that such type of models could be used very efficiently to simulate the electronic devices and circuits fabricated with submicron technology