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Inertial MEMS sensors (accelerometers, gyroscopes, magnetometers) are used for real time motion analysis, orientation and trajectory estimation. Commercial-off-the-shelf sensors are cheap enough, however, not accurate. The main sources of errors are short term noises, bias instability and temperature dependence. In order to produce an industrial grade inertial measurement unit, MEMS sensors with lower noise, smaller bias and higher temperature stability should be used. Sensors in a production batch differ from each other. It is reasonable to carry out specific testing for sensors to be pre-selected. The paper describes experimentally found distributions of parameters for more than 250 MPU9250 chips (each including a three axial accelerometer, gyroscope and magnetometer). MPU9250 (MPU9255) is among the inertial chips widely used in hand held devices. To estimate accelerometers biases a specific calibration procedure was used. It includes raw data acquisition from a sensor module in stand still periods. The module was fixed on a plastic icosahedron, which was rotated and placed iteratively on all its planes. Temperature profiles were registered on an experimental set up with a Peltier element. Classification of temperature curves was carried out with machine learning approach. Criteria for sensors pre-selection for specific applications are discussed.