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In this paper Dynamic ATC has been calculated using energy function based Potential Energy Boundary Surface (PEBS) method. For the effective use of power system under the deregulated environment, it is important to make a fast and accurate evaluation of the maximum available transfer capability (ATC). Transient stability assessment by time domain simulation method is a time consuming process. A novel Dynamic contingency screening method is discussed and critical contingencies are selected for the computation of Dynamic ATC in order to reduce the computational time. The information about the ATC is to be continuously updated in real-time and made available to the market participants through Open Access Same time Information System (OASIS). On the basis of ATC, Independent System Operator (ISO) evaluates the transaction. Thus the ATC must be computed fast and accurately. In this paper ATC has been computed for real time applications using two different neural networks viz., i) Back Propagation Algorithm (BPA) and ii) Radial Basis Function (RBF) Neural Network These two methods are tested on WSCC 3 Machine 9 bus system and New England 10 machine 39 bus system and results are compared with the conventional energy function based PEBS method.