The detection of structural similarities of process models is frequently discussed in the literature. The state-of-the-art approaches for structural similarities of process models presume a known subgraph that is searched in a larger graph, and utilize behavioral and textual semantics to achieve their goal. In this paper we propose an approach to detect reoccurring structures in a collection of BPMN2.0 process models, without the knowledge of a subgraph to be searched, and by focusing solely on the structural characteristics of the process models. The proposed approach deals with the problems of subgraph isomorphism, frequent pattern discovery and maximum common subgraph isomorphism, which are mentioned as NP-hard in the literature. In this work we present a formal model and a novel algorithm for the detection of reoccurring structures in a collection of BPMN 2.0 process models. We then apply the algorithm to a collection of 1,806 real-world process models and provide a quantitative and qualitative analysis of the results.