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This thesis uses multiple layer perceptrons (MLP) neural networks and Kohonen clustering networks to predict and assign confidence to nonlinear time series classifications. The nonlinear time series used for analysis is the Standard and Poor's 100 (S&P 100) index. The target prediction is classification of the daily index change. Financial indicators were evaluated to determine the most useful combination of features for input into the networks. After evaluation it was determined that net changes in the index over time and three short-term indicators result in better accuracy. A back-propagation trained MLP neural network was then trained with these features to get a daily classification prediction of up or down. Next, a Kohonen clustering network was trained to develop 30 different clusters. The predictions from the MLP network were labeled as correct or incorrect within each classification and counted in each category to determine a confidence for a given cluster. Test data was then run through both networks and predictions were assigned a confidence based on which cluster they belonged to. The results of these tests show that this method can improve the accuracy of predictions from 51% to 73%. Within a cluster accuracy is near 100% for some classifications.