We propose a method, SDpop, able to infer sex-linkage caused by recombination suppression typical of sex chromosomes. The method is based on the modeling of the allele and genotype frequencies of individuals of known sex in natural populations. It is implemented in a hierarchical probabilistic framework, accounting for different sources of error. It allows statistical testing for the presence or absence of sex chromosomes, and detection of sex-linked genes based on the posterior probabilities in the model. Furthermore, for gametologous sequences, the haplotype and level of nucleotide polymorphism of each copy can be inferred, as well as the divergence between them. We test the method using simulated data, as well as data from both a relatively recent and an old sex chromosome system (the plant Silene latifolia and humans), and show that, for most cases, robust predictions are obtained with 5 to 10 individuals per sex.