In this release of SNNS, three model selection criteria are implemented: the Schwarz's Bayesian criterion (SBC), Akaikes information criterion (AIC) and the conservative mean square error of prediction (CMSEP). The SBC, the default criterion, is more conservative compared to the AIC. Thus, pruning via the SBC will produce smaller networks than pruning via the AIC. Be aware that both SBC and AIC are selection criteria for linear models, whereas the CMSEP does not rely on any statistical theory, but happens to work pretty well in an application. These selection criteria for linear model can sometimes directly be applied to nonlinear models, if the sample size is large.