The paper considers the duality theory in linear vectorial programming by using the Lagrangean approach without scalarization. The dual problem has set-valued nature. The aim of the paper is to use methods of set-valued optimization for deriving duality assertions in linear vector optimization. A case study parallel to the scalar linear optimization is presented. The proofs are based onto a separation property introduced by the authors. The main features of the theory are illustrated by simple examples.