Microarrays are a tool for measuring the expression levels of a large number of genes simultaneously. In the microarray experiment, however, many undesirable systematic variations are observed. Correct identification and removal of these variations is essential to allow the comparison of expression levels across experiments. We describe the use of linear mixed models for the normalization of two-color spotted microarrays for various sources of variation including printtip variation. Normalization with linear mixed models provides a parametric model of which results compare favorably to intensity dependent normalization LOWESS methods. We illustrate the use of this technique on two datasets. The first dataset contains 24 arrays, each with approximately 600 genes, replicated 3 times per array. A second dataset, coming from the apo AI experiment, was used to further illustrate the methods. Finally, a simulation study was done to compare between methods.