he use of acoustic emission (AE) signal in machinery condition has considerable interest due to AE signal characteristics that can refer to machine condition. However, selecting correct AE parameters playing a pivotal role in machinery condition monitoring. This study proposed a methodology of selecting the best parameters of AE based on multivariate analysis of variance (MANOVA) method. The study aiming at monitoring or modeling enhancement by quantitatively measuring the divergence of AE parameters acquired from 72 operational conditions of industrial reciprocating compressor. In this case, nine out of thirteen AE parameters are selected as the most sensitive parameter to the compressor operational conditions according to MANOVA eta squared (η2). Eventually, the authors believe that using this method can enhance the monitoring or modelling using AE parameter in the field of machinery condition monitoring
