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Current microarray technologies are able to assay thousands of samples over million of SNPs simultaneously. Computational approaches have been developed to analyse a huge amount of data from microarray chips to understand sophisticated human genomes. The data from microarray chips might contain errors due to bad samples or bad SNPs. In this paper, we propose a method to detect bad SNPs from the probe intensities data of Illumina Beadchips. This approach measures the difference among results determined by three software Illuminus, GenoSNP and Gencall to detect the unstable SNPs. Experiment with SNP data in chromosome 20 of Kenyan people demonstrates the usefulness of our method. This approach reduces the number of SNPs that are needed to check manually. Furthermore, it has the ability in detecting bad SNPs that have not been recognized by other criteria.