What data are you analysing? If the data are ratio-scale measurements, then you should consider using the 2-way parametric anova, even if the normality assumption is not met. There are two main reasons. First, the parametric anova is considered robust to violations in the normality assumption. Second, if you use a Friedman test on ratio-scale data, information is lost. Statistical power in the test is sacrificed.
On the first point, try doing a Google search. Have fun navigating through the arguments on each side!
On the second point, let me give a simple example, since many folks either don't consider this or don't understand the issue. If you have three conditions A, B, and C and measured reponse times of, for example, A=40 seconds, B=39 seconds, and C = 20 seconds, you can resonably observe that "A and B took much longer than C". However, this information is lost when doing a Friedman test, because the data are collapsed into ranks: A=1, B=2, and C=3.
