In addition to the need of satisfying several competing objectives, many real-world applications are also characterized by a certain degree of noise, manifesting itself in the form of signal distortion or uncertain information. While studies have shown that many multi-objective evolutionary optimizers are capable of achieving optimization goals, their ability to deal with noise is rarely studied. In this paper, extensive studies are carried out to examine the impact of noisy environments in evolutionary multi-objective optimization based upon five benchmark problems characterized by different difficulties in local optimality, non-uniformity, discontinuity and non-convexity. Interestingly, the baseline algorithm employed tends to evolve better solution sets in the presence of low noise levels for some problems. Nevertheless, the evolutionary optimization process degenerates into random search under increasing noise levels.