Asymptotic Properties of the Maximum Likelihood Estimators in the Nonlinear Regression Model with Normal Errors In this paper we consider a set y = (y1,... ,y) of observations, not necessarily independent on identically distributed, whose joint distribution is known to be normal, where both the mean vector p or the covariance matrix 2 may depend on unknown parameters 11,...,y (=y) to be estimated. By putting Pt(Y) = (P(xt.Y) we obtain the nonlinear regression model (t=1,2,...,n), c = N(0,0(y)) Yt = 43(xt'l) ct as a special case. In the present paper we will obtain conditions for the asymptotic normality of a consistent maximum likelihood estimator of y .