This paper presents COnfECt, a model learning approach, which aims at recovering the functioning of a black box system from its execution traces. COnfECt is specialised into the detection of components of a black-box system and in the inference of models called systems of LTSs. For every component dis- covered, COnfECt generates a Labelled Transition System (LTS), which captures its behaviours. Besides, it synchronises the LTSs together to express the func- tioning 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 synchronises them. We describe the three steps of COnfECt and the related algorithms in this paper. Then, we present some preliminary experimentations on a real system.