In vivo variations of blood glucose (BG) are affecting the biophysical characteristics (e.g. dielectric and optical) of skin and underlying tissue (SAUT) at various frequencies. However, the skin impedance spectra for instance can also be affected by other factors, perturbing the glucose related information, factors such as temperature, skin moisture and sweat, blood perfusion as well as body movements affecting the sensor-skin contact. In order to be able to correct for such perturbing factors, a Multisensor system was developed including sensors to measure the identified factors. To evaluate the quality of glucose monitoring, the Multisensor was applied in 10 patients with Type 1 diabetes. Glucose was administered orally to induce hyperglycaemic excursions at two different study visits. For analysis of the sensor signals, a global multiple linear regression model was derived. The respective coefficients of the variables were determined from the sensor signals of this first study visit (R(2)=0.74, MARD=18.0%--mean absolute relative difference). The identical set of modelling coefficients of the first study visit was re-applied to the test data of the second study visit to evaluate the predictive power of the model (R(2)=0.68, MARD=27.3%). It appears as if the Multisensor together with the global linear regression model applied, allows for tracking glucose changes non-invasively in patients with diabetes without requiring new model coefficients for each visit. Confirmation of these findings in a larger study group and under less experimentally controlled conditions is required for understanding whether a global parameterisation routine is feasible.