The LIPOMETER is an optical device for measuring the thickness of a subcutaneous adipose tissue layer. It illuminates the interesting layer, measures the backscattered light signals and from these, it computes absolute values of subcutaneous adipose tissue layer thickness (in mm). Previously, these light pattern values were fitted by nonlinear regression analysis to absolute values provided by computed tomography. Nonlinear regression analysis might provide slight limitations for our problem: a selected curve type cannot be changed afterwards during the application of the measurement device. Artificial neural networks yield a more flexible approach to this fitting problem and might be able to refine the fitting results. In the present paper we compare nonlinear regression analysis with the behaviour of different architectures of multilayer feed forward neural networks trained by error back propagation. Specifically, we are interested whether neural networks are able to yield a better fit of the LIPOMETER light patterns to absolute subcutaneous adipose tissue layer thicknesses than the nonlinear regression techniques. Different architectures of these networks are able to surpass the best result of regression analysis in training and test, providing higher correlation coefficients, regression lines with absolute values obtained from computed tomography closer to the line of identity, decreased sums of absolute and squared deviations, and higher measurement agreement.