"This test can be used to test for goodness of fit. A sample is selected from an unknown population and its goodness of fit to a hypothetical model of the population must be tested. Both the sample and and the hypothetical model are plotted together in cumulative form, each scaled so their cumulative sums are 1.0. We then look for the greatest difference between the two; this is the KolmogorovSmirnov statistic, KS. Table 1 gives critical values for the KS statistic. If the computed test statistic does not fall into the critical region then we cannot reject the null hypothesis. Conversely, if the statistic does fall into the critical region then we can reject the null hypothesis." (from Chuck's book need info)
Click here for a very useful site to perform the KolmogorovSmirnov test.
SAMPLE
SIZE 
LEVEL OF SIGNIFICANCE FOR D = MAXIMUM [ F_{0}(X)  S_{n}(X) ] 

.20 
.15 
.10 
.05 
.01 

1 
.900 
.925 
.950 
.975 
.995 
2 
.684 
.726 
.776 
.842 
.929 
3 
.565 
.597 
.642 
.708 
.828 
4 
.494 
.525 
.564 
.624 
.733 
5 
.446 
.474 
.510 
.565 
.669 
6 
.410 
.436 
.470 
.521 
.618 
7 
.381 
.405 
.438 
.486 
.577 
8 
.358 
.381 
.411 
.457 
.543 
9 
.339 
.360 
.388 
.432 
.514 
10 
.322 
.342 
.368 
.410 
.490 
11 
.307 
.326 
.352 
.391 
.468 
12 
.295 
.313 
.338 
.375 
.450 
13 
.284 
.302 
.325 
.361 
.433 
14 
.274 
.292 
.314 
.349 
.418 
15 
.266 
.283 
.304 
.338 
.404 
16 
.258 
.274 
.295 
.328 
.392 
17 
.250 
.266 
.286 
.318 
.381 
18 
.244 
.259 
.278 
.309 
.371 
19 
.237 
.252 
.272 
.301 
.363 
20 
.231 
.246 
.264 
.294 
.356 
25 
.210 
.220 
.240 
.270 
.320 
30 
.190 
.200 
.220 
.240 
.290 
35 
.180 
.190 
.210 
.230 
.270 
OVER 35 
1.07 
1.14 
1.22 
1.36 
1.63 