Reference curves which take one covariable into account such as the age, are often required in medicine, but simple systematic and e±cient statistical methods for constructing them are lacking. Classical methods are based on parametric Øtting (polynomial curves). In this talk, we describe a method- ology for the estimation of reference curves for data sets, based on nonpara- metric estimation of conditional quantiles. The derived method should be applicable to all clinical or more generally biological variables that are mea- sured on a continuous quantitative scale. To avoid the curse of dimensionality when the covariate is multidimensional, a semiparametric approach is pro- posed. This procedure combines a dimension-reduction step (based on sliced inverse regression) and kernel estimation of conditional quantiles step. The usefulness of this semiparametric estimation procedure is illustrated on a sim- ulated data set and on a real data set collected in order to establish reference curves for biophysical properties of the skin of healthy French women