The paper presents control strategies for the active steering of solid axle railway vehicles using the linear quadratic Gaussian (LQG) method. The paper investigates the benefits of actively controlling and steering the wheelsets of a railway vehicle and studies what could be achieved when modern control techniques are used on the vehicles via mechatronic components. An optimal H2 controller is developed for the active steering and is fine-tuned using genetic algorithms. A Kalman filter is developed to provide the full state feedback required by the optimal control. The Kalman filter is formulated in such a way that it not only estimates all the vehicle states, but also calculates parameters such as curve radius and cant of the railway track on which the vehicle is travelling. Computer simulations are used in the study to assess the system performance with the control scheme proposed.