In system reliability analysis, it is frequently necessary to resort t
o Monte Carlo simulation (MCS). The importance sampling method (ISM) i
s an advanced MCS method that may greatly improve the efficiency and a
ccuracy of simulation approaches. Its application to structural system
reliability analysis is focused here. Importance of proper determinat
ion of the sampling density is emphasized based on a critical review o
f other suggested sampling densities. A new sampling density, the weig
hted general normal sampling density (WGNSD), is proposed. This densit
y is proportional to the ideally optimal one at the most important poi
nts of the variable space, and it is general for structural system rel
iability analysis. A variety of applications of this method arc presen
ted for illustration, as well as for demonstration of success with the
proposed sampling density, including nondifferentiable failure surfac
es and higher-dimensional problems.