Microarray technology has become a powerful tool to analyze the gene expression of tens of thousands of genes simultaneously. Microarray based gene expression profiles are available for malignant thyroid tumors (i.e. follicular thyroid carcinoma, and papillary thyroid carcinoma), and for benign thyroid tumors (such as autonomously functioning thyroid nodules, and cold thyroid nodules). In general, the two main foci of microarray investigations are improved understanding of the pathophysiology / molecular etiology of thyroid neoplasia and the detection of genetic markers that could improve the differential diagnosis of thyroid tumors. Their results revealed new features, not known from one gene studies. Simultaneously, the increasing number of microarray analyses of different thyroid pathologies raises the demand to efficiently compare the data. However, the use of different microarray platforms complicates cross-analysis, in addition, there are other important differences between these studies: (i) some studies use intra-individual comparisons whereas other studies perform inter-individual comparisons, (ii) the reference tissue is defined as strictly non-nodular healthy tissue or contains also benign lesions such as goiter, follicular adenoma, and hyperplastic nodules in some studies, (iii) the widely used Affymetrix GeneChip platform comprises several GeneChip generation that are only partially compatible. Moreover, the different studies are characterized by strong differences in data analysis methods, which vary from simple empiric filters to sophisticated statistic algorithms. Therefore, this review summarizes and compares the different published reports in the context of their study design. It also illustrates perspectives and solutions for data set integration and meta-analysis as well as the possibilities to combine array analysis with other genetic approaches.