Machine learning is a popular tool for building state of the art software systems. It is more and more used also in safety critical areas. This demands for verification techniques ensuring the safety and security of machine learning based solutions. However, in this presentation, we argue that the popularity of machine learning comes from the fact that no formal specification exists which renders traditional verification in appropriate. Instead, validation is typically demanded and we present a recent technique that validates certain correctness properties for an underlying recurrent neural network.