The fouling of venturi meters, used for steam generator feedwater flow rate measurement in pressurized water reactors (PWRs), may result in unnecessary plant power derating. On-line monitoring of these important instrument channels and the thermal efficiencies of the balance-of-plant components are addressed. The steam generator feedwater flow rate and thermal efficiencies of critical components in a PWR are estimated by means of artificial neural networks. The physics of these systems and appropriate plant measurements are combined to establish robust neural network models for on-line prediction of feedwater flow rate and thermal efficiency of feedwater heaters in PWRs. A statistical sensitivity analysis technique was developed to establish the performance of this methodology.