There are relatively few works dealing with conformal prediction for multi-task learning issues, and this is particularly true for multi-target regression. This paper focuses on the problem of providing valid (i.e., frequency calibrated) multi-variate predictions. To do so, we propose to use copula functions for inductive conformal prediction, and illustrate our proposal by applying it to deep neural networks and random forests. We show that the proposed method ensures efficiency and validity for multi-target regression problems on various data sets.