Photon correlation spectroscopy (PCS) is widely used for nanoparticle characterization, being particularly suited for the analysis of narrow distributions. However, because of its simplicity, it would be important to extend it to other situations, namely to wide particle size distributions and to bimodal samples. In this work an evaluation of the discriminating capability of the most common algorithms used in PCS to deconvolute the measured auto-correlation function, when analyzing bimodal samples, was performed. The results show that the CONTIN algorithm enables a better description of bimodal mixtures, though its performance is dependent on the characteristics of the sample and on operating conditions such as the sampling time. Additionally, a close analysis of the auto-correlation function revealed the existence of systematic deviations in the slope of the first section of that function when a bimodal sample is compared with the equivalent monomodal one (same average diameter). The results obtained indicate that the inspection of the first part of the auto-correlation function produces important information that can reveal the presence of more than one peak when an unknown sample is analyzed. This feature is rather important since the inversion process may mask one of the peaks due to the ill-conditioned nature of the problem. This prior information can be useful to alert the user to the need for an iterative adjustment of the parameters of the inversion process in order to better reveal all the peaks in the sample.