  Given a class of functions $F$ on a probability space $(\Omega,\mu)$, we
study the structure of a typical coordinate projection of the class, defined by
$\{(f(X_i))_{i=1}^N : f \in F\}$, where $X_1,...,X_N$ are independent, selected
according to $\mu$. This notion of projection generalizes the standard linear
random projection used in Asymptotic Geometric Analysis.
  We show that when $F$ is a subgaussian class of functions, a typical
coordinate projection satisfies a Dvoretzky type theorem.
