This study was undertaken to investigate the effect of moisture content (MC) on the prediction accuracy of soil organic C (SOC) and pH of soils collected from Turkey and the United Kingdom using a fiber-type visible and near infrared (Vis-NIR) spectrophotometer. The diffuse reflectance spectra of 270 soil samples were measured under six gravimetric MC levels of 0, 5, 10, 15, 20, and 25%. Partial least squares (PLS) regression analyses with full cross-validation were performed to establish models for SOC and pH. Before PLS analysis, the entire spectra were randomly split three times into calibration (80%) and validation (20%) sets. Results showed that the prediction performance of SOC in the validation set was successful, with root mean square errors of prediction (RMSEPs) of 1.26 to 1.55% and residual prediction deviations (RPDs) of 2.29 to 2.83, and rather poor for pH, with RMSEPs of 0.65 to 0.85 and RPDs of 1.29 to 1.65. The best accuracy achieved for SOC was for dry soil samples (RMSEP = 1.26%, RPD = 2.83), whereas the worst accuracy was for wet soil samples with 5% MC (RMSEP = 1.55%, RPD = 2.29). The best result for pH was obtained for dry samples (RMSEP = 0.70%, RPD = 1.65), although this accuracy was comparable to that of the 10% MC soil samples (RMSEP = 0.65%, RPD = 1.60). The ANOVA supported the conclusion that there was a significant effect of MC on prediction accuracy, although this effect was larger for SOC (P < 0.0000) than pH (P < 0.05).