  It is shown the almost sure convergence and asymptotical normality of a
generalization of Kesten's stochastic approximation algorithm for
multidimensional case. In this generalization, the step increases or decreases
if the scalar product of two subsequente increments of the estimates is
positive or negative. This rule is intended to accelerate the entrance in the
`stochastic behaviour' when initial conditions cause the algorithm to behave in
a `deterministic fashion' for the starting iterations.
