This monograph aims at presenting the core weak convergence theory for sequences of random vectors with values in \(\mathbb{R}^k\). In some places, a more general formulation in metric spaces is provided. It lays out the necessary foundation that paves the way to applications in particular sub-fields of the theory. In particular, the needs of Asymptotic Statistics are addressed. A whole chapter is devoted to the weak convergence theory in \(\mathbb{R}\) where specific tools, for example for handling weak convergence of sequences using independent and identically distributed random variables such that the Renyi's representations by means of standard uniform or exponential random variables, are stated. The functional empirical process is presented as a powerful tool for solving a considerable number of asymptotic problems in Statistics. The text is written in a self-contained approach with the proofs of all used results at the exception of the general Skorohod-Wichura Theorem. We finish the book with a chapter on weak convergence of bounded measures and locally bounded measures in preparation of a more general theory of measures on topological spaces.