Despite its notoriously slow learning time, back-propagation (BP) is one of the most widely used neural network training algorithms. Two major reasons for this slow convergence are the step size problem and the flat spot problem [Fahlman, 1988]. In [Samad, 1991] a simple modification, the expected source values (ESV) rule, is proposed for speeding up the BP algorithm. We have extended the ESV rule by coupling it with a flat-spot removal strategy presented in [Fahlman, 1988], as well as incorporating a momentum term to combat the step size problem. The resulting rule has shown dramatically improved learning time over standard BP, measured in training epochs. Two versions of the ESV modification are mentioned in [Samad. 1991], on-demand and up-front, but simulation results are given mostly for the on-demand case. Our results indicate that the up-front version works somewhat better than the on-demand version in terms of learning speed. We have also analyzed the interactions between the three modifications as they are used in various combinations.