Tumors of central nervous system (CNS) represent a unique challenge in diagnosis and treatment because of their heterogeneous phenotypic and genotypic behavior. Unambiguous characterization of these tumors is essential towards accurate prognosis and therapy. Rapid advancements in microarray technologies have made it very promising to achieve this unambiguous characterization. In this work, we propose a procedure for classifying Central Nervous System (CNS) tumors based on DNA microarray gene expressions of samples from patients with a variety of CNS tumor types. After obtaining the tumor specific gene expression estimates, significantly differentially expressed (marker) genes are located. Then, the genes are clustered using a complete linkage hierarchical algorithm. The algorithm involves clustering together all the genes that show high correlation in their expression measures across the samples. From such gene-cluster, eigengene expressions are obtained by projecting the expression values of the genes within the same cluster onto their first three principal components. In the final step of building prototype for any particular tumor type, the corresponding tissue samples with eigengene expressions are divided into subgroups using self-organizing map (SOM). The set of centroids of the subgroups is used as the prototype of the corresponding tumor type. In predicting the tumor type of a new tissue sample, distances are calculated between the new sample and all the centroids of all the tumor prototypes. The new tissue sample is classified to the tumor type of the nearest centroid. Experimental results reported in this work strongly support the histological categorization of the tumors and the current knowledge of their molecular definitions.