Past studies have already shown that stars in open clusters are chemically homogeneous (e.g. De Silva et al. 2006, 2007 and 2009). These results support the idea that stars born from the same giant molecular cloud should have the same chemical composition. In this context, the chemical tagging technique was proposed by Freeman et al. (2002). The principle is to recover disrupted stellar clusters by looking only to the stellar chemical composition. In order to evaluate the feasibility of this approach, it is necessary to test if we can distinguish between stars born from different molecular clouds. For this purpose, we studied the chemical composition of stars in 32 old and intermediate-age open clusters, and we applied machine learning algorithms to recover the original cluster by only considering the chemical signatures.