This paper introduces a consistent specification test for semiparametric conditional densities. This test is motivated by the fact that many important econometric models need to be estimated through maximum likelihood type procedures, e.g.~semiparametric limited dependent variable models. This specification is also important for prediction purposes. Our statistic combines the methodology of goodness of fit tests and nonparametric methods. It is shown to be asymptotically distributed standard normal under the null hypothesis if the semiparametric model is correctly specified. Further, the test is shown to have power against $n^{-1/2}h^{-d/2}$ local alternatives to the null hypothesis. We discuss practical issues for the application statistics and illustrate in an intensive monte carlo study both the feasibility and the performance of the procedure.