This paper presents asymptotic properties of the maximum pseudo-likelihood
estimator of a vector \Vect{\theta} parameterizing a stationary Gibbs point
process. Sufficient conditions, expressed in terms of the local energy function
defining a Gibbs point process, to establish strong consistency and asymptotic
normality results of this estimator depending on a single realization, are
presented.These results are general enough to no longer require the local
stability and the linearity in terms of the parameters of the local energy
function. We consider characteristic examples of such models, the Lennard-Jones
and the finite range Lennard-Jones models. We show that the different
assumptions ensuring the consistency are satisfied for both models whereas the
assumptions ensuring the asymptotic normality are fulfilled only for the finite
range Lennard-Jones model
