Marker genes are defined as genes in which the expression level characterizes a specific experimental condition. Such genes in which the expression levels differ significantly between different groups are highly informative relevant to the studied phenomenon. In this paper, first the system can detect marker genes that are selected by ranking genes according to statistics after normalizing data with methods that are the most widely used among several normalization methods proposed the while, And it compare and analyze a performance of each of normalization methods with mult-perceptron neural network layer. The Result that apply Multi-Layer perceptron algorithm at Microarray data set including eight of marker gene that are selected using ANOVA method after Lowess normalization represent the highest classification accuracy of 99.32% and the lowest prediction error estimate.