The purpose of this study is to propose a diagnostic approach to identify engineering students' English reading comprehension errors. Student data were collected during the process of reading texts of English for science and technology on a web-based cumulative sentence analysis system. For the analysis, the association-rule, data mining technique was applied to mine students' reading errors. Specific association rules of reading errors were identified and distinctive patterns of error production have been recognized among different groups of students. This article addresses the issue of English reading difficulty frequently found among engineering students and discovered possible tendencies of student reading errors. By using the techniques in this article to identify students' reading problems, instructors will be able to construct learning materials for adaptive learning, and thus reduce the cost of teaching and facilitate students' learning outcome. Pedagogical implications were provided based on the results of the study.