Reconstruction-based fault isolation, which explores the underlying fault characteristics and uses them to isolate the cause of the fault, has attracted special attention. However, it does not explore how the specific process variables change and which ones are most significantly disturbed under the influences of abnormality; thus, it may not be helpful to understanding the specifics of the fault process. In the present work, an efficient faulty variable selection algorithm is proposed that can detect the significant faulty variables that cover the most common fault effects and thus significantly contribute to fault monitoring. They are distinguished from the general variables that are deemed to follow normal rules and thus are uninformative to reveal fault effects. To further reveal the fault characteristics, the selected significant faulty variables are then chosen to obtain a parsimonious reconstruction model for fault isolation in which relative analysis is performed on these selected faulty variables to explore the relative changes from normal to fault condition. The faulty variable selection can not only focus more on the responsible variables but also exclude the influences of uninformative variables and thus probe more effectively into fault effects. It can also help in finding a more interesting and reliable model representation and better identify the underlying fault information. Its feasibility is illustrated with simulated faults using data from the Tennessee Eastman (TE) benchmark process. (C) 2015 Elsevier Ltd. All rights reserved.