Fault identification using the emitted mechanical noise is becoming an attractive field of research in a variety of industries. It is essential to rank acoustic feature integration functions on their efficiency to classify different types of sound for conducting a fault diagnosis. The Mel frequency cepstral coefficient (MFCC) method was used to obtain various acoustic feature sets in the current study. MFCCs represent the audio signal power spectrum and capture the timbral information of sounds. The objective of this study is to introduce a method for the selection of statistical indicators to integrate the MFCC feature sets. Two purpose-built audio datasets for squeak and rattle were created for the study. Data were collected experimentally to investigate the feature sets of 256 recordings from 8 different rattle classes and 144 recordings from 12 different squeak classes. The support vector machine method was used to evaluate the classifier accuracy with individual feature sets. The outcome of this study shows the best performing statistical feature sets for the squeak and rattle audio datasets. The method discussed in this pilot study is to be adapted to the development of a vehicle faulty sound recognition algorithm.