  We produce an explicit parameterization of well-rounded sublattices of the
hexagonal lattice in the plane, splitting them into similarity classes. We use
this parameterization to study the number, the greatest minimal norm, and the
highest signal-to-noise ratio of well-rounded sublattices of the hexagonal
lattice of a fixed index. This investigation parallels earlier work by
Bernstein, Sloane, and Wright where similar questions were addressed on the
space of all sublattices of the hexagonal lattice. Our restriction is motivated
by the importance of well-rounded lattices for discrete optimization problems.
Finally, we also discuss the existence of a natural combinatorial structure on
the set of similarity classes of well-rounded sublattices of the hexagonal
lattice, induced by the action of a certain matrix monoid.
