The automatic color equalization (ACE) algorithm, part of the Retinex-derived family of spatial color algorithms (SCAs), is an image enhancement algorithm that mimics the adjustment behavior of the human visual system (HVS). It is commonly used on a unique channel for black & white images or independently on the three channels for color images. In this work, we introduce a novel application of ACE on hyperspectral images, referred to as HyperspectrACE. Here the goal is not to introduce the most performing hyperspectral image enhancer in the field, but to discuss the performance of a qualitative model of human color sensation when applied on more than the standard RGB channels. For this reason, we present the test of the proposed approach compared with classic ACE and other classic methods, to assess the differences in image dynamic stretching and global and local filtering contrast adjustment.