Nowadays, a lot of related data obtained from these research could be given a new present meaning to accomplish the original purpose of the whole research as a human project. The method of tumor classification based on microarray could contribute to being accurate tumor classification by finding differently expressing gene pattern statistically according to a tumor type. Therefore, the process to select a closely related informative gene with a particular tumor classification to classify tumor using present microarray technology with effect is essential. In this thesis, we used cDNA microarrays of 3840 genes obtained from neuronal differentiation experiment of cortical stem cells on white mouse with cancer, constructed accurate tumor classification model by extracting informative gene list through normalization separately and then did performance estimation by analyzing and comparing each of the experiment results. Result classifying Multi-Perceptron classifier for selected genes using Pearson correlation coefficient represented the accuracy of 95.6%.