Romain,

The numbers of samples have nothing to do with the applicability of 
Hotelling-s t-square.

If you believe the underlying populations are multivariate normal *with 
the same variance-covariance* *matrix*, T-square is an appropriate 
test.  The assumption of equal VC matrices is more stringent than the 
univariate assumption of equal variance.  For the multivariate test, the 
two populations are also assumed to have the same shapes.  My experience 
is that same shape is often an issue.

Which approach to use?
1) what question do you want to ask?  Do the populations have the same 
mean vectors? Or, do you want to identify which Y component differs?  
Either approach answers the first question, although I believe T-square 
has slightly higher power than multiple testing adjusted t-tests.  
Univariate t-tests answer the second.
2) How strongly do you believe in multivariate normality, especially the 
same shape part of that assumption?

Fran James and Chuck McCulloch wrote a very nice summary of multivariate 
statistics in ecology for Annual Reviews in 1990.  It's old but much of 
the advice is still relevant.  Much of my thinking on the choice of test 
is based on their paper.

Best wishes,
Philip Dixon




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