Diabetes is a disorder of metabolism that has affected 18.2 million people in the United States. In recent years, researchers have identified many genes that play important roles in the onset, development and progression of diabetes. Identification of these diabetes genes offers better understanding of the molecular mechanisms underlying pathogenesis, which is essential for developing preventative and therapeutic methods. In this paper, we propose an innovative approach, fuzzy membership test (FM-test), based on fuzzy set theory to identify diabetes associated genes from microarray gene expression profiles. A new concept of FM d-value is defined to quantify the divergence of two sets of values. Experiments were conducted to study the distribution of d-values and the relationship between the d-value and the significance level of p-value. We applied FM-test to a gene expression dataset obtained from insulin-sensitive and insulin-resistant people and identified ten significant genes. Six of the ten have been confirmed to be associated with diabetes in the literature and one has been suggested by other researchers. The remaining three genes, D85181, m95610, AND U06452, are suggested as potential diabetes genes for further biological investigation.