  One way to interpret the classical secretary problem (CSP) is to consider it
as a special case of the following problem. We observe $n$ independent
indicator variables $I_1,I_2,\dotsc,I_n$ sequentially and we try to stop on the
last variable being equal to 1. If $I_k=1$ it means that the $k$-th observed
secretary has smaller rank than all previous ones (and therefore is a better
secretary). In the CSP $p_k=E(I_k)=1/k$ and the last $k$ with $I_k=1$ stands
for the best candidate. The more general problem of stopping on a last "1" was
studied by Bruss(2000). In what we will call Weber's problem the variables
$I_k$ can take more than two values and we try to stop on the last occurence of
\textit{one} of these values. Notice that we do not know in advance the value
taken by the variable on which we stop.
  We can solve this problem in some cases and provide algorithms to compute the
optimal stopping rule. These cases carry enough generality to be applicable in
concrete situations.
