Quadrotors are a type of aircraft that lately has gained increased popularity within the UAV-scientific area. Many research groups around the world have implemented control systems that allow for autonomous flight of quadrotors with the help of the known dynamics. This thesis presents two approaches to modelling the dynamics of the quadrotor. The first is a linear greybox approach where the structure is derived from known equations and some constants are measured and some identified through system identification techniques. The second model is a blackbox model where a neural network is trained and used. The two models are then evaluated using known error measurements with the help of previously recorded flight data and the results are presented. It is for example shown that with the untreated flight data the traditional greybox model have accurate dynamics but is sensitive to noise and drifts in the measurements. It is also shown that better results generally can be achieved using a neural network model, especially for noisy unpreprocessed data.