The basic idea of process mining is to extract knowledge from event logs recorded by an information system. Until recently, the information in these event logs was rarely used to analyze the underlying processes. Process mining aims at improving this by providing techniques and tools for discovering process, organizational, social, and performance information from event logs. Fuelled by the omnipresence of event logs in transactional information systems (cf. WFM, ERP, CRM, SCM, and B2B systems), process mining has become a vivid research area [1, 2]. In this paper we introduce the challenging process mining domain and discuss a heuristics driven process mining algorithm; the so-called “HeuristicsMiner” in detail. HeuristicsMiner is a practical applicable mining algorithm that can deal with noise, and can be used to express the main behavior (ie not all details and exceptions) registered in an event log. In the experimental section of this paper we introduce benchmark material (12.000 different event logs) and measurements by which the performance of process mining algorithms can be measured.