Spectrum prediction of C-13 NMR spectra is a versatile tool during structure elucidation. A wide range of methods including increment calculation, HOSE-code derived correlation tables and neural network technology has been described in the literature during the last four decades. The basic concepts of these algorithms will be discussed using examples from natural product chemistry, Most of the methods are restricted to a two-dimensional model of structure description neglecting stereochemical features which contribute substantially to chemical shift values. Our approach of deriving steric interactions from a 2.5-dimensional structure representation (up/down-bonds) and utilizing this information during spectrum prediction will be shown. Some useful applications based on this algorithm and also some statistical evaluations derived from our database holding 116,000 C-13 NMR-spectra will be presented.