This work aims at investigating the influence of luminance information and environment illumination on skin classification. We explore Bayesian approaches to perform automatic classification of human skin pixels on digital images, using color features as input. Two probabilistic skin color models were built on different color spaces (RGB, normalized RG, HSI, HS, YCbCr and CbCr) and tested in a task of automatic pixel classification into skin and non-skin. Analyses of classification performance were done by presenting an illumination controlled image database containing images acquired in four different illumination conditions (shadow, sun, incandescent and fluorescent lights) to these classifiers. Our experiments show that building probabilistic skin color models using the CbCr color space generally improves performance of the classifiers and that best performance is achieved in shadow illumination.