Vehicle dynamics control (VDC) for motorcycles had a fast growth during the last 10 years. The available technologies comprise curve-safe ABS and traction control (TC) systems, anti-wheelie control, right up to comprehensive motorcycle stability systems including even more control functions. VDC systems rely on real-time information about the current motorcycle dynamic state. Thus motorcycles are equipped with additional sensor units, namely MEMS inertial measurement devices, capable of gathering accelerations and angular rates. The application of model-based estimation theory enables the determination of the necessary information about the in-plane and out-of-plane motion, e.g. the motorcycle lean angle. Since VDC systems include safety critical control functions, the validation within simulations including sensor characteristics is mandatory. The MEMS accelerometer and gyroscope features include low-cost and small footprint, however there are considerable stochastic sensor errors to cope with. In this study the characteristic of different MEMS sensors and their noise models are investigated. The sensor noise terms are identified by analyzing measurement data using the Allan variance method. Different sensors are compared and the stochastic noise coefficients are quantified. The sensor noises are modeled with according random processes defined by linear time-invariant systems and white-noise inputs. As a result, the obtained stochastic sensor models can be used for model-based estimation and control algorithm design, as well as verification within simulation environments.