Combining statistical and relational learning receives currently a lot of attention. The majority of statistical relational learning approaches focus on density estimation. For classification, however, it is well-known that the performance of such generative models is often lower than that of discriminative classifiers. One approach to improve the performance of generative models is to combine them with discriminative algorithms. Fisher kernels were developed to combine them with kernel methods, and have shown promising results for the combinations of support vector machines with (logical) hidden Markov models and Bayesian networks. So far, however, Fisher kernels have not been considered for relational data, i.e., data consisting of a collection of objects and relational among these objects. In this paper, we develop Fisher kernels for relational data and empirically show that they can significantly improve over the results achieved without Fisher kernels.