Elastomeric vibration mounts are used in many industries to reduce the overall vibrations, thereby improving the human comfort and extending the lifetime of the system. Accurate prediction of vibrations in full systems requires correct identification of the material parameters and modelling. However, as the manufacturing process influences the material properties of elastomeric materials, material parameter identification from coupon tests is not fully reliable for predicting the component behaviour. Therefore, component tests are performed for the material identification, requiring Finite Element simulations to compare experimental and simulated deformations. Elastomeric materials exhibit non-linear, time- and frequency-dependent visco-hyperelastic behaviour, requiring the generalised-α FE method to simulate the dynamic behaviour. The material parameters are identified from component tests using FE simulations within a gradient-based optimisation scheme. Due to the iterative nature of the non-linear simulations, an efficient sensitivity calculation is performed using the adjoint variable method. This method incorporates the equations of motion into the objective function using adjoint variables. These equations are then solved backwards in time to determine the adjoint variables, and thereby eliminating the dependency of the displacement states on the material parameters. The sensitivity of the objective function with respect to the material parameters is then calculated using these adjoint variables. This method results in the computational cost of the sensitivity calculation to be independent of the number of material parameters. This is an important characteristic as modelling the non-linear and frequency dependent behaviour over large frequency ranges requires the use of ten or more parameters. This method enables component manufacturers to identify the material properties of their vibration mounts directly, removing the need for special coupons. It also allows for a more accurate prediction of the component behaviour as the material behaviour is identified closer to the operating conditions.