Two separate artificial sensors, an electronic eye (EE) and an electronic tongue (ET), were recently developed to monitor grape ripening based on the analysis of must. The aim of this research is to exploit the complementary information obtained by means of EE and ET sensing systems using different data fusion strategies, in order to develop an integrated device able to quickly and easily quantify the physico-chemical parameters that are used to assess phenolic ripeness. To this purpose, both low-level and mid-level data fusion approaches were investigated. Partial Least Squares (PLS) regression was applied to the fused data, with the aim of relating the information brought by the two sensors with twelve physico-chemical parameters measured on the must samples by standard analytical methods. The results achieved with mid-level data fusion outperformed those obtained using EE and ET separately, and highlighted that both the artificial sensors have made a significant contribution to the prediction of each one of the considered physico-chemical parameters. Display omitted •Data fusion was used to improve the performance of a device to monitor grape ripening.•Data from an electronic tongue (ET) and an electronic eye (EE) were combined.•Low-level and mid-level data fusion were used to build PLS calibration models.•The use of data fusion allowed to increase significantly the prediction performances.•Mid-level data fusion of PLS latent variables gave generally the best results.