I suppose it depends on what you mean by "robust", there are different ways the process can go awry. If your residuals are distributed symmetrically, but simply not normally, and you don't have other issues that you don't mention (e.g., missing data, this isn't a factorial ANOVA as @JeromyAnglim points out), then your parameter estimates should be unbiased. On the other hand, depending on how far your data differ from normality, 42 - 55 may not be enough for the Central Limit Theorem to kick in and cover for you. That is, your p-values may be off. How far off (as you may have guessed from the previous sentence), will depend on how much your residuals differ from normality, with just small differences, you're probably fine. On a slightly different note, remember that if your variances are not equal the test will not be maximally efficient (read: reduced power). One other tip: with respect to non-normality, skew, especially with different cells skewed in different directions, is worse than having excess kurtosis not equal to 0.
Update: Unless you have a clear reason for believing that your data come from some other specific distribution (e.g., they're counts), the question is simply about the skewness and the excess kurtosis. The best discussion I've seen of these issues is here. Note that under skewness -> interpreting, there are some arbitrary guidelines that may be helpful, and that under kurtosis -> visualizing, you can see what the possible range of values is [-2, $\infty$). Again, the issue is: will the Central Limit Theorem cover for you, and that depends on the manner and extent of non-normality and how much data you have. Answering that analytically is going to be extremely difficult, although it's not too bad via simulation. I've run some simulations using distributions like the uniform and the chi-squared with df=1, to explain the idea of the CLT; even from these very non-normal distributions, the sampling distribution of the mean converges to normal impressively fast, IMO. Thus, my guess is that if you only have a little bit of skewness, you are probably fine, given your sample sizes, but of course I can't give you a final, analytical answer.
The issue with unequal cell sizes in factorial ANOVA is that the factors are correlated with each other. That means that using standard tests (which amounts to using type III sums of squares) don't properly use all of the information available. I discuss these issues here, with some complementary information here.