One hundred and eighty eight blueberry fruit and leaf samples were obtained from a commercial blueberry field in Waldo, Florida in June, 2010. Spectral reflectance was measured in the ultraviolet (UV), visible and near-infrared (NIR) ranges (200 nm-2500 nm) with an increment of 1 nm for each sample. Five different categories (mature fruit, intermediate fruit, immature fruit, light-green leaf and dark-green leaf) were used for classification model construction and validation. Significant differences in reflectance among the five categories were found in the visible and NIR region. Especially, the mature fruit had much lower reflectance in both regions, which shows great potential for distinguishing mature fruit from other categories. Based on the spectral characteristics of each category, fourteen normalized vegetation indices were developed for further statistical analysis to find significant bands for classifying different fruit maturity status as well as leaves. Principal component analysis (PCA), classification regression tree and multinomial logistic regression were conducted to develop prediction models for distinguishing different classes. The multinomial logistic regression model with three independent variables, which are the combinations of reflectance at six wavelengths (500, 525, 550, 575, 680, and 750 nm) performed the best, with prediction accuracy of 100%. The six wavelengths thus can be used for developing an easy-to-use and low cost fruit maturity sensor for a blueberry yield mapping system.