Two-dimensional gel electrophoresis (2DE) is an indispensable tool in proteomics for the analysis of protein expression in complex biological systems such as cells and tissues. However, the automatic extraction of information from gel images is still a challenging task. In this paper we propose a strategy that represents a computational procedure of support to the discrimination of different clinical conditions associated with the samples. The analyzed gel images were acquired within the framework of a study of peripheral neuropathies: twenty-four 2DE maps generated from cerebrospinal fluid (16 pathologic and 8 control subjects) were processed. Quantitative features were defined to describe each image and treated with a method of dimensionality reduction. The informativeness of the descriptors allowed us to see the gel of the data set as items in a three-dimensional space, segregating according to the clinical conditions. Moreover, information with prognostic value was obtained for a single outsider gel of a patient who was included in a clinical subgroup at the first diagnosis but whose disease progressed with clinical features belonging to a different clinical subgroup. The method developed may represent an effective tool of classification that can be used repeatedly to capture the essential impression from separation images.