Model misspecifcation is a serious issue since misspecifcation generally renders statistical inference invalid. However, specifcation testing of discrete choice models is rarely applied. This paper describes a nonparametric test procedure which uses a combination of smoothed residual plots and a test statistic able to detect general misspecifcation. Nonparametric methods require large datasets when the number of independent variables is more than a few. A way to circumvent this problem is indicated, increasing the usefulness of the approach also with limited datasets.