The purpose of this dissertation is to analyze dynamic behavior of a stressed power system and to correlate the dynamic responses to a near future system voltage abnormality. It is postulated that the dynamic response of a stressed power system in a short period of time-in seconds-contains sufficient information that will allow prediction of voltage abnormality in future time-in minutes. The PSSE dynamics simulator is used to study the dynamics of the IEEE 39 Bus equivalent test system. To correlate dynamic behavior to system voltage abnormality, this research utilizes two different pattern recognition methods one being algorithmic method known as Regularized Least Square Classification (RLSC) pattern recognition and the other being a statistical method known as Classification and Regression Tree (CART). Dynamics of a stressed test system is captured by introducing numerous contingencies, by driving the system to the point of abnormal operation, and by identifying those simulated contingencies that cause system voltage abnormality. Normal and abnormal voltage cases are simulated using the PSSE dynamics tool. The results of simulation from PSSE dynamics will be divided into two sets of training and testing set data. Each of the two sets of data includes both normal and abnormal voltage cases that are used for development and validation of a discriminator. This research uses stressed system simulation results to train two RLSC and CART pattern recognition models using the training set obtained from the dynamic simulation data. After the training phase, the trained pattern recognition algorithm will be validated using the remainder of data obtained from simulation of the stressed system. This process will determine the prominent features and parameters in the process of classification of normal and abnormal voltage cases from dynamic simulation data. Each of the algorithmic or statistical pattern recognition methods have their advantages and disadvantages and it is the intention of this dissertation to use them only to find correlations between the dynamic behavior of a stressed system in response to severe contingencies and the outcome of the system behavior in a few minutes into the future.