Multi-task learning is a domain that is still not fully studied in the conformal prediction framework, and this is particularly true for multi-target regression. Our work uses inductive conformal prediction along with deep neural networks to handle multi-target regression by exploring multiple extensions of existing single-target non-conformity measures and proposing new ones. This paper presents our approaches to work with conformal prediction in the multiple regression setting, as well as the results of our conducted experiments.