This thesis introduces a new analysis (PSSOM) of the output map of Kohonen's Self-Organizing Map. Using this analysis we are able to use the SOM as a supervised net. PSSOM's major advantage is its ability to assign degrees of classification certainty to unseen test data compared to the conventional supervised network that gives crisp decisions. This thesis also investigates the applications of this analysis as a first level in a hybrid neural network model. The experiments show how the PSSOM can be used at the top of a hierarchical classifier model on one hand to increase considerably the overall speed of network training without losing accuracy or on the other hand to increase the overall accuracy of the system without increasing its computational cost.