Efficient and reliable available bandwidth measurement remains an important goal for many applications. In this paper we introduce an empirical bandwidth estimation tool based on neural networks. Training the neural network on simulation data, it provides reliable estimation of physical and available bandwidth for simulated single and multi-hop networks, in laboratory environment and among the real world conditions of the ETOMIC testbed.