A common approach in dealing with inverse heat conduction problems is to use regularization in an attempt to smooth the estimated heat flux as a function of time. If the unknown heat flux is known to be a series of discrete pulses, however, smoothing is undesirable. In dealing with cases such as these, any reduction in the ill-posed composition of the problem is extremely helpful. The present research, utilizing the method of derivative regularization, employs the principle of matrix pre-multiplication to reduce the ill-conditioned nature of the matrix structure. The use of a pre-conditioning method is fairly straightforward; however, the difficult and most critical aspect of this method is the development of the pre-conditioning matrices for the specific type of problem. As the name implies, derivative regularization employs the time derivatives of the sensitivity coefficients, and of the measured data, in developing a pre-conditioning matrix to reduce the ill-posed nature of the inverse heat conduction problem. While the additive forms of regularization generally introduce bias, the derivative regularization method presented here carries the advantage of being unbiased. However, this unbiased advantage can sometimes come at the cost of larger estimation errors. In this research, the inverse heat conduction problem is used for studying the results of tests over a full range of weighting factors. Additionally, various degrees of measurement errors are added to the data in order to observe the effects of measurement errors on the performance of derivative regularization method.