This paper presents an efficient technique for analyzing inverse heat conduction problems using a Kalman Filter-enhanced Bayesian Back Propagation Neural Network (KF-(BPNN)-P-2). The training data required for the KF-(BPNN)-P-2 are prepared using the Continuous-time analogue Hopfield Neural Network and the performance of the KF-(BPNN)-P-2 scheme is then examined in a series of numerical simulations. The results show that the proposed method can predict the unknown parameters in the current inverse problems with an acceptable error. The performance of the KF-(BPNN)-P-2 scheme is shown to be better than that of a stand-alone Back Propagation Neural Network trained using the Levenberg-Marquardt algorithm. (c) 2006 Elsevier Ltd. All rights reserved.