Context. Stars are born together from giant molecular clouds and, if we assume that they werechemically homogeneous and well-mixed, we expect them to share the same chemical composition.Most of the stellar aggregates are disrupted while orbiting the Galaxy and the dynamic informationis lost, thus the only possibility to reconstruct the stellar formation history is to analyze the chemicalabundances that we observe today.Aims. The chemical tagging technique aims to recover disrupted stellar clusters based merely ontheir chemical composition. We evaluate the viability of this technique to recover conatal stars that arenot gravitationally bound anymore.Methods. We built a high-quality stellar spectra library to facilitate the assessment of spectralanalyses. We developed our own spectral analysis framework, named iSpec, capable of homogeneizingstellar spectra and deriving atmospheric parameters/chemical abundances. Finally, we compiledstellar spectra from 32 Open Clusters, homogeneously derived atmospheric parameters and 17 abundancespecies, and applied machine learning algorithms to group the stars based on their chemicalcomposition. This approach allows us to evaluate the viability of the chemical tagging technique.Results. We found that stars in dierent evolutionary stages have distinguished chemical patternsmay be due to NLTE eects, atomic diusion, mixing and correlations from atmospheric parameterdeterminations. When separating stars per evolutionary stage, we observed a high degree of overlappingamong Open Cluster’s chemical signatures, making it dicult to recover conatal aggregates byapplying the chemical tagging technique.Keywords : spectroscopy, open clusters, chemical abundances, stars, Galaxy.