1. At a computer system including one or more processors and system memory, a method for adapting the neural architecture of neural networks to detect abnormalities in the operation of a monitored system, the method comprising: an act of accessing a historical data set for a system, the historical data set including a plurality of monitored measurands indicative of measurements of prior performance for the system, the plurality of measurands for use in training neural networks to monitor the ongoing performance of the system; for each measurand: an act of automatically determining the measurand type of the measurand from the historical data, the measurand type selected from among: continuous, discrete, and constant; and an act of automatically scaling and normalizing the measurand based upon the behavior of one or more of: the maximum value, the minimum value, the mean value, the standard deviation value, the slope value, and the frequency value for the measurand over user selected temporal windows in the historical data; an act of configuring and training a set of neural networks to detect abnormal events in the monitored measurands, including for each neural network in the set of networks: an act of configuring the neural network to monitor the system for abnormalities related to the correlated group of measurands subsequent to scaling and normalizing the measurands, configuring the neural network in layers including: an act of selecting the number of the input layer nodes for the input layer of neural network; an act of automatically adapting the number of hidden layer nodes for the hidden layer of the neural network based on training convergence behaviors of the neural networks of the measurands in the correlated group of measurands, including: an act of identifying a subset of the historical data for hidden layer sizing; and an act of optimizing the number of hidden layer nodes based on the sequence of behaviors of the neural networks training convergence for all measurands in the correlated group within the historical data subset using a hidden layer size search algorithm that efficiently determines how the hidden layer size space is searched; an act of selecting the number of output layer nodes for the output layer of the neural network; an act of assigning one or more connections between input layer nodes and hidden layer nodes; and an act of assigning one or more connections between hidden layer nodes and output layer nodes; and an act of applying the trained set of neural networks to a new data set or real-time data where reports of abnormalities are produced from the new data set.