This dissertation demonstrates a novel experimental technique that makes it possible to see and capture high voltage transmission line transients in real time. The benefit of this technique is that it uses existing power systems equipment, so that no new high voltage construction is required. With the data captured using this technique, it is possible to determine the cause of the transient activity, with a focus on lightning as a source. A high speed Cascade Feed Forward Back Propagating Artificial Neural Network was developed to provide an unsupervised solution to the transient data. This information would be a valuable asset to any utility company because it would provide insight into cause of faults so that mitigation of these transient sources could then be dealt with accordingly. This dissertation summarizes the creation of the experiment, evaluation of the results, building the software models of the experiment and then development of a high speed neural network for classification of the transients.