Image preprocessing is useful in helping to identify `spectral response patterns' for certain types of image classification problems. The common artifacts in remotely sensed images are caused by the blurring due to the optics of the image gathering device, illumination variations, and the radiative transfer of the atmosphere. The Multi-Scale Retinex (MSR) image enhancement algorithm that provides dynamic range compression, reduced dependence on lighting conditions, and improved (perceived) spatial resolution has proven to be an effective tool in the correction of image degradations such as those in remote sensing images. In this paper, we measure the improvement in classification accuracy due to the application of the MSR algorithm. We use simulated images generated with different scene irradiance and with known ground truth data. The simulation results show that, despite the degree of image degradation due to changes in atmospheric irradiance, classification error can be substantially reduced by preprocessing the image data with the MSR. Furthermore we show that, similar to the results achieved in previous work, the classification results obtained from the MSR preprocessed images for various scene irradiance are more similar to each other than are the classification results for the original unprocessed images. This is evident in the observed visual quality of the MSR enhanced images even before classification is performed, and in the different images obtained by comparing image data under different irradiance conditions. We conclude that the application of the MSR algorithm results in improved visual quality and increased spatial variation of multispectral images that is also optimal for certain types of multispectral image classification.