In multiarray experiments, there is some system bias, which contaminated by experimental factors such as spot location (often referred to as a print-tip effect), arrays, dyes, and various interactions of these effects. For comparable each other, it is necessary to normalize the raw expression profile data. Normalization is the key step in low level processing. In fact, many normalization methods have been developed, i.e. Scaling normalization, Nonlinear normalization, Quantile normalization and so on. New baseline normalization is presented. First, select the subset of probes, which have the min rank range. Second, do nonlinear normalization on robust baseline. Iterative strategy weakens the sensitivity of the baseline method to select baseline. With the standard test dataset, compare it with other methods. The results show that the novel method has better performances than others in several ways.