Given that knowledge regarding the etiology of comorbidity between disorders can have a significant impact on research regarding the classification, treatment, and etiology of the disorders, the ability to reject incorrect hypotheses regarding the causes of comorbidity is very important. A simulation study was conducted to assess the validity of the Neale and Kendler (1995) model-fitting approach in examining the etiology of comorbidity between two disorders. First, data were simulated under the assumptions of the 13 alternative comorbidity models described by Neale and Kendler. Second, model-fitting analyses testing the comorbidity models were conducted on the simulated datasets. Thirteen sets of data with varying model parameters were simulated to test Neale and Kendler's assertion that their model-fitting approach is appropriate across a range of potential prevalences and degrees of familiality. The validity of the model-fitting approach in examining unselected twin data and a combination of selected family data and unselected family data was explored. The model-fitting approach successfully discriminated several classes of comorbidity models, although discrimination between models within classes of related models was less accurate. Results suggest that the model-fitting approach can be a useful tool in examining the etiology of the comorbidity between disorders if the caveats of the present study's results are considered carefully. As predicted by Neale and Kendler, variations in the disorder prevalences and familial correlations did not affect the validity of their model-fitting approach, but affected the power to discriminate the correct model. As suggested by Neale and Kendler, the model-fitting approach can be applied to both unselected and selected data and to both twin and family data.