How do you identify the variables that separate several groups? I don't have much background on statistics. I am working on multivariate morphometrics of a sample of frogs. I have a data matrix of 19 variables (continuous characteristics) for around 250 samples. The samples fall into 4 different groups (morphotypes).
Is PCA the best way to see if the samples belonging to one group cluster together? How can one identify the variables that would contribute the most to separation of the clusters? What other methods could help in a study like this?
 A: The first question is whether you already know which frog belongs to which morphotype If you do know, and your goal is to use these frogs to better analyze how the morphotypes vary on these variables, then you want discriminant analysis. This might enable later investigators to accurately place frogs into morphotypes based on these variables. 
If you do not know which frog belongs to which morphotype, then cluster analysis may be useful. 
Both these methods have a lot of options and subtypes. 
A: I think that you know the group membership so as @PeterFlom said discriminant analysis is a good altternative. A similar method would be to estimate a Multinomial (logit or probit) model. In this model, you estimate the probability of clasyfing a frog into a given $k$ group depending on its characteristics $x$.
$P[G=k]=\Phi(\sum \beta_j^k x_j)$ 
where $\Phi$ is the probability distribution function you assume. The upper script on the beta parameters shows that each characteristic has a different impact on the possibility of classification at different groups.
The most simple version of this model is the multinomial logit and there are several extensions to it. I guess that's an affordable  start if you are relatively new into statistics.
