Currently, failure-based risk assessments in the process industry do not empirically take into account the type of chemicals processed in equipment, mainly because chemical-specific failure rate data barely exist. This paper suggests a methodology to calibrate failure-based risk assessment predicated on the chemical being processed in equipment. The methodology uses a data mining tool known as the association rule. Specifically, the lift association rule is utilized (the Lift Methodology). By extracting equipment failure information from incident databases based on the chemical involved in the process, the Lift Methodology leads to more accurate equipment-related risk assessment. (C) 2006 Elsevier Ltd. All rights reserved.