1. A computer implemented process for training neural networks to their optimal training point, the process comprising the steps of: initializing neural network node weight values; dividing a set of neural network training data into a training set and a test set, said training data having input and output values; applying said training set to said neural network and adjusting said weight values in response to an output of said applying said training set; applying said test set to said neural network with said adjusted weight values; calculating an output variance based upon said test set and an output of said applying said test set; testing said variance by i) storing a first variance as a minimum variance; ii) comparing each calculated variance with said minimum variance; iii) replacing said minimum variance with said calculated variance if said calculated variance is less than said stored minimum variance; iv) storing a variance trend wherein said variance trend comprises a user determined number of past calculated variance values; and v) signaling that said variance has reached a minimum if said variance trend is increasing from said stored minimum variance; and in response to said testing step, terminating said training if said testing indicates said variance has reached a minimum, or repeating the applying said training set, applying said test set, calculating an output variance and testing said variance steps if said variance has not reached a minimum.