Different chemometric approaches were used to determine generic patterns in the temporal and spatial variations in the coastal water quality of the northern Yellow Sea off Yantai, China. Hierarchical cluster analysis grouped the 16 months into two periods (i.e. March-October and November-February), reflecting strong seasonality in the data, and grouped the 12 sampling sites into two clusters (i.e. outside Yantai Bay and inside Yantai Bay), based on similarities in water quality characteristics. Discriminant analysis gave the best results for data complexity reduction during temporal analysis, but not during spatial analysis. Discriminant analysis identified five significant parameters (water temperature, salinity, and concentrations of dissolved inorganic nitrogen, dissolved inorganic phosphate and dissolved silicate) affording about 97.9% correct assignations in temporal analysis. In addition, principal component analysis identified three varifactors that explained 71% of temporal changes in the coastal water quality data set. Overall, the present study showed that these multivariate statistic methods were effective for evaluating temporal and spatial variations in the coastal water quality of Yantai. Water temperature and nutrient inputs may be major driving factors for the trophic status of these coastal waters. Low variances in spatial patterns of water quality parameters of Yantai were mostly related to the unrestricted water exchange between Sishili Bay and the Yellow Sea.