We propose an adaptive multiresolution (MR) approach for classification of fluorescence microscopy images of subcellular protein locations, providing biologically relevant information. These images have highly localized features both in space and frequency which naturally leads us to MR tools. Moreover, as the goal of the classification system is to distinguish between various protein classes, we aim for features adapted to individual proteins. These two requirements further lead us to adaptive MR tools. We start with a simple classification system consisting of Haralick texture feature computation followed by a maximum-likelihood classifier, and demonstrate that, by adding an MR block in front, we are able to raise the average classification accuracy by roughly 10%. We conclude that selecting features in MR subspaces allows us to custom-build discriminative feature sets for fluorescence microscopy images of protein subcellular location images.