In this paper, we construct a stochastic particles method for the Burgers equation with a monotonic initial condition; we prove that the convergence rate is $\displaystyleO\left(\frac1\sqrtN +\sqrt\D\right)$ for the $L^1(I\!\!R \times \Omega)$-norm of the error. To obtain that result, we link the PDE and the algorithm to a system of weakly interacting stochastic particles; the difficulty of the analysis comes from the discontinuity of the interaction kernel, equal to the Heaviside function. In~\citebossy_talay-93, we show how the algorithm and the result extend to the case of non monotonic initial conditions for the Burgers equation; we also treat the case of nonlinear PDE's related to particles systems with Lipschitz interaction kernels. Our next objective is to adapt our methodology to the (more difficult) case of the 2-D inviscid Navier-Stokes equation.