This paper addresses the problem of combining auditory representations of an acoustic signal based on a measure of the correlation of spectral events between representations in a way which attempts to preserve the detailed spatio-temporal events present in the auditory data. The fuzzy set theory class membership function is used to provide a measure of closeness of an observation vector to a set of centres positioned in the data space to characterise perceptually important spectral events. The technique provides a level of tolerance to the position of spectral events and allows a simple form of noise adaptation. A recognition experiment is described which demonstrates the improvement in noise robustness achieved for an isolated digit database at varying levels of signal to noise ratio (SNR) over a traditional MFCC pre-processor.-