This paper presents COnfECt, a model learning approach, which aims at recovering the functioning of a component-based system from its execution traces. We refer here to non concurrent systems whose internal interactions among components are not observable from the environment. COnfECt is specialised into the detection of components of a black-box system and in the inference of models called systems of labelled transition systems (LTS). COnfECt tries to detect components and their specific behaviours in traces, then it generates LTS for every component discovered, which captures its behaviours. Besides, it synchronises the LTSs together to express the functioning of the whole system. COnfECt relies on machine learning techniques to build models: it uses the notion of correlation among actions in traces to detect component behaviours and exploits a clustering technique to merge similar LTSs and synchronise them. We describe the three steps of COnfECt and the related algorithms in this paper. Then, we present some preliminary experimentations.