n-dimensional(hypercube)distance calculation..

The `centers' are the means?  by() can find the mean of multivariate data
by group.  And dist() finds Euclidean and other distances.

However, the Jeffries-Matusita distance depends on covariance matrices,
and 50 points in 100 dims are not enough to estimate one.  Indeed my 
concern is that you have so few data that either the measurements are 
highly correlated (so you can just select a few) or your inferences will 
be suspect.

I am rather new in R so i would appreciate your help in my problem..

I have 3 types of vegetation (A,B,C),50 measurements per class and 100 
variables per measurement.

It would be helpful to know how you have stored them.

I would like to perform seperability analysis between these classes meaning...

a.)create the hypercube from these 100 variables

Which hypercube?  If you mean the bounding box, use apply or lapply with
range().

b.)"plot" the 50 measurements for each class and identify the position of the
center of each class..
c.)calculate the distances between each class center using Euclidean,
Jeffries-Matusita or other measures.

Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595