This paper deals with the influence of pre-whitening for the task of fundamental frequency estimation in noisy conditions. Parametric fundamental frequency estimators commonly assume that the noise is white and Gaussian and, therefore, they are only statistically efficient under those conditions. The noise is coloured in many practical applications and this will often result in problems of misidentifying an integer divisor or multiple of the true fundamental frequency (i.e., octave errors). The purpose of this paper is to see if pre-whitening can reduce this problem, based on noise statistics obtained from existing noise PSD estimation algorithms. For this purpose, different noise types and prediction orders of LPC pre-whitening are considered. The results show that pre-whitening improves significantly the estimation accuracy of an NLS pitch estimator when the noise is fairly stationary. For nonstationary noise, the improvements are modest at best, but we hypothesize that this is due to the noise PSD estimation performance rather than the LPC pre-whitening principle.