Viola and Jones [VJ] demonstrate that cascade classification methods can
successfully detect objects belonging to a single class, such as faces.
Detecting and identifying objects that belong to any of a set of ``classes'',
"many class detection", is a much more challenging problem.
We show that objects from each class
can form a ``cluster'' in a ``classifier space''
and illustrate examples of such clusters
using images of real world objects.
Our detection algorithm uses a ``decision tree classifier''
(whose internal nodes each correspond to a VJ classifier)
to propose a class label for every sub-image "W" of a test image
(or reject it as a negative instance).
If this "W" reaches a leaf of this tree,
we then pass "W" through a subsequent VJ cascade of classifiers,
specific to the identified class,
to determine whether "W" is truly an instance of the proposed class.
We perform several empirical studies
to compare our system for detecting objects of any of "M" classes,
to the obvious approach of running a "set" of "M"
learned VJ cascade classifiers, one for each class of objects,
on the same image.  We found that the detection rates are comparable,