PROC LIFETEST is a nonparametric approach to the analysis of survival data, meaning it makes no assumption about the underlying distribution. To quote SAS online documentation: "You can use the LIFETEST procedure to compute nonparametric estimates of the survivor functions, to compare survival curves, and to compute rank tests for association of the failure time variable with covariates."

You mention you have groups. If your 'groups' are treated as strata, you can test for a difference in SURVIVOR FUNCTIONS between the strata if you use the following code: proc lifetest data=yodata plots = (s) graphics; time timeVAR * censVAR(0); strata yourgroup; run; you will get to see a neat survival function plot, a log-rank test of equality of survival function over strata.

However, to test for which distribution fits your data best (log- normal, exponential, log-logistic, weibull) one option is the LIFEREG procedure. For an example of testing model fits, please see [VA Lung Cancer Data: Example 2] at http://tinyurl.com/2dfpdm

While you are at this, be sure look into model nesting (e.g. exponential is a special case of Weibull) and try to fit the most parsimonious of several competing models.