In a recent paper, Barzilai and Borwein presented a new choice of steplength for the gradient method. Their choice does not guarantee descent in the objective function and greatly speeds up the convergence of the method. We derive an interesting relationship between any gradient method and the shifted power method. This relationship allows us to establish the convergence of the Barzilai and Borwein method when applied to the problem of minimizing any strictly convex quadratic function (Barzilai and Borwein considered only 2-dimensional problems). Our point of view also allows us to explain the remarkable improvement obtained by using this new choice of steplength. For the two eigenvalues case we present some very interesting convergence rate results. We show that our Q and R-rate of convergence analysis is sharp and we compare it with the Barzilai and Borwein analysis. We derive the preconditioned Barzilai and Borwein method and present preliminary numerical results indicating that it is an effective method, as compared to the preconditioned Conjugate Gradient method, for the numerical solution of some special symmetric positive definite linear systems that arise in the numerical solution of Partial Differential Equations.