We present Temporal Pattern Search (TPS), a novel algorithm for searching for temporal patterns of events in historical personal histories. The traditional method of searching for such patterns uses an automaton-based approach over a single array of events, sorted by time stamps. Instead, TPS operates on a set of arrays, where each array contains all events of the same type, sorted by time stamps. TPS searches for a particular item in the pattern using a binary search over the appropriate arrays. Although binary search is considerably more expensive per item, it allows TPS to skip many unnecessary events in personal histories. We show that TPS's running time is bounded by O(m^2 n lg(n)), where m is the number of items in a search pattern, and n is the number of events in a history. Although the asymptotic running time of TPS is inferior to that of a non-deterministic finite automaton (NFA) approach (O(mn)), TPS performs better than NFA under our experimental conditions. We also show TPS is very competitive to Shift-And, a bit-parallelism approach, with real data. Since the experimental conditions we describe here subsume the conditions under which analysts would typically use TPS (i.e. within an interactive visualization program), we argue that TPS is an appropriate design choice for us.