Power system dynamic security is a growing concern in today's utilities industry. It is generally recognized that current frameworks for dynamic security assessment are not capable of meeting the industry's needs. The interest, therefore, has focused on a new tool of analysis that offers a new framework for assessing power system dynamic security, and which includes the trend in the security status;In this dissertation a new framework for power system security and vulnerability assessment has been developed. Within this framework, system vulnerability is addressed as a new concept for assessing the system dynamic security. The transient energy function (TEF) method was used as a tool to develop this new framework. The new framework can indicate both the present security level using the energy margin [delta]V, and the trend of security status due to the possible variation of a system operating parameter p using the energy margin sensitivity [partial][delta] V/[partial] p. Therefore, this framework can inform us about the weakest point in the system and assess how the changes of the parameter will cause the system to become vulnerable;The indices of vulnerability are determined by establishing the thresholds for acceptable levels of [delta]V and [partial][delta] V/[partial] p; and relating these thresholds to stability limits of critical system parameters;The artificial neural networks (ANNs) technique and the selected multi-layered perceptron architecture are applied to this framework for fast pattern recognition and classification of security status for on-line analysis;The proposed procedure for assessing the system vulnerability and the multi-layered perceptron neural network are tested on the IEEE 50 generator test system. The preliminary results are very promising.