The flexibility of MIL's blob analysis module proved to be indispensable during the development of the Neural Network Classifier. "The blob analysis functions provide many input measurements that are easy to calculate and easily accessed within our program. Measurements that are not needed are easily removed and don't add to the processing time. Having access to the blob (defect) location allows us to make our own specialized measurements when necessary," explains Gibbons. As powerful as they are, Neural Networks can only work if they are based on solid data; if they are trained on inconsistent data, they will give inconsistent results "Neural Networks fully respect the old computer saying: garbage in, garbage out. We apply statistical tests to select the best blob input features and remove inconsistent data from the training sets. When properly used, Neural Networks can produce very robust results," adds Gibbons.