The application of genome-wide approaches to the molecular characterization of cancer was investigated, identifying footprints that can potentially assist in the subclassification of tumors in order to contribute to diagnosis and clinical management of patients. High resolution DNA copy number analysis by single nucleotide polymorphism mapping array technology has been widely applied to study copy number aberrations and to distinguish among different loss of heterozigosity mechanisms associated with or without copy number changes in tumors. However, assessment of statistically significant common aberrations across the whole data set or a subset of tumor samples is still an open problem. Therefore, we adapted the recently developed STAC algorithm, previously applied to comparative genomic hybridization data, to identify common copy number aberrations in renal carcinoma samples using Affymetrix 100K SNP arrays. SNP copy number data were processed by a homebrew pipeline implemented in R and analyzed using STAC.