Data mining techniques can be used to effectively interact with large databases and assist in solving problems related to drought by extracting information from massive data archives. These techniques have been used for commercial application, medical research, and telecommunications, but never for drought. Two new data mining algorithms [i.e., Representative Episodal Association Rule (REAR) and Minimal Occurrences With Constraints And Time Lags (MOWCATL)] have been developed to identify the relationships between oceanic parameters and drought indices to monitor drought. The Palmer Drought Severity Index (PDSI) is used to define drought conditions for Clay Center in south-central Nebraska to demonstrate the influence of oceanic parameters, such as El Nino and Southern Oscillation (ENSO), on drought. The oceanic indices used include the Multivariate ENSO Index (MEI), North Atlantic Oscillation index (NAO), Pacific North Atlantic index (PNA), and Southern Oscillation Index (SOI). The results show that the association rules generated with oceanic indices using time-series data mining algorithms may be used to monitor drought.