Counterfeit watches are products of illicit activity and contain traces of their production and distribution. Traces provide pertinent information through one of their fundamental characteristics: the ability to reveal links between specimens or cases. The aim of this study was to develop an analytical strategy to obtain the elemental composition of watchcases, by analysing a selection of 35 counterfeit watches. We propose a methodology based on multivariate statistical analysis of chemical results that discriminates between watches from common and different origins, and, ultimately, classifies them into chemical groups. All watchcases were analysed using inductively coupled plasma mass spectrometry (ICP-MS), providing representative descriptive data on the composition of watchcases. Several multivariate approaches were assessed, considering different scenarios, each using a different set of variables. It appeared that the model that performed best in terms of classification criteria could be misleading, especially in an exploratory context that focuses on the production of intelligence. At the end of the day, hierarchical cluster analysis (HCA) allowed us to classify the specimens into 14 chemical classes. Information gained through chemical analysis revealed several links between the specimens. This initial study was performed on a very limited number of watches. Although still in the developmental stage, our approach exhibits promising capabilities and encourages chemical profiling of counterfeit watches on larger scale.