  In the context of this paper, a record is an entry in a sequence of random
variables (RV's) that is larger or smaller than all previous entries. After a
brief review of the classic theory of records, which is largely restricted to
sequences of independent and identically distributed (i.i.d.) RV's, new results
for sequences of independent RV's with distributions that broaden or sharpen
with time are presented. In particular, we show that when the width of the
distribution grows as a power law in time $n$, the mean number of records is
asymptotically of order $\ln n$ for distributions with a power law tail (the
\textit{Fr\'echet class} of extremal value statistics), of order $(\ln n)^2$
for distributions of exponential type (\textit{Gumbel class}), and of order
$n^{1/(\nu+1)}$ for distributions of bounded support (\textit{Weibull class}),
where the exponent $\nu$ describes the behaviour of the distribution at the
upper (or lower) boundary. Simulations are presented which indicate that, in
contrast to the i.i.d. case, the sequence of record breaking events is
correlated in such a way that the variance of the number of records is
asymptotically smaller than the mean.
