Abstract This paper introduces the multiple domain‐invariant partial least squares (mdi‐PLS) method, which generalizes the recently introduced domain‐invariant partial least squares method (di‐PLS). In contrast to di‐PLS which solely allows transferring of knowledge from a single source to a single target domain, the proposed approach enables the incorporation of data from an arbitrary number of domains. Additionally, mdi‐PLS offers a high level of flexibility by accepting labeled (supervised) and unlabeled (unsupervised) data to cope with dataset shifts. We demonstrate the application of the mdi‐PLS method on a simulated and one real‐world dataset. Our results show a clear outperformance of both PLS and di‐PLS when data from multiple related domains are available for training multivariate calibration models underpinning the benefit of mdi‐PLS.