We describe a heuristic search-based method for interleaved HTTP (Web) session reconstruction building upon first order Markov models. An interleaved session is generated by a user who is concurrently browsing the same web site in two or more web sessions (browser tabs or windows). In order to assure data quality for subsequent phases in analyzing user's browsing behavior, such sessions need to be separated in advance. We propose a separating process based on best-first search and trained first order Markov chains. We develop a testing method based on various measures of reconstructed sessions similarity to original ones. We evaluate the developed method on two real world click stream data sources: a web shop and a university student records information system. Preliminary results show that the proposed method performs well.