In one-sample applications is the theoretical distribution and is the empirically observed distribution. Alternatively the two distributions can both be empirically estimated ones; this is called the two-sample case.
The Cramér-von-Mises test is an alternative to the Kolmogorov-Smirnov test. It is thought that the CvM test is more powerful than the KS test, but this has not been shown theoretically.
Cramér-von-Mises test (one sample)
Let be the observed values, in increasing order. Then it is possible to show that
If this value is larger than the tabulated value we can reject the hypothesis that the data come from the distribution .
Cramér-von-Mises test (two samples)
Let and be the observed values in the first and second sample respectively, in increasing order. Let be the ranks of the x's in the combined sample, and let be the ranks of the y's in the combined sample. It can be shown that
where U is defined as
If the value of T is larger than the tabulated value we can reject the hypothesis that the two samples come from the same distribution. (Some books give critical values for U, which is more convenient, as it avoids the need to compute T via the expression above. The conclusion will be the same).
Anderson: 'On the Distribution of the Two-Sample Cramer-von Mises Criterion', Annals Math. Stat. 33, #3 (1962), p1148-1159. 
Xiao, Gordon, Yakovlev: 'A C++ program for the Cramér-von-Mises two sample test', Journal of Statistical Software, 17 #8, January 2007