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Conditioned Latin Hypercube Sampling (cLHS) is a type of stratified random sampling that accurately represents the variability of environmental covariates in feature space. As the smallest possible sample is important for efficient field work, what is the optimal sample size for digital soil mapping? An optimal sample size accurately represents the variability in the environmental covariates and provides enough samples for predictive models. This paper briefly reviews cLHS and investigates different sample sizes for representing five environmental covariates in a 30,000-ha complex landscape in the Great Basin of southwestern Utah. The cLHS code was run in Matlab (TM) (Mathworks, 2008) and statistical analysis was performed using the R statistical language (R Development Core Team, 2009). Graphical analysis for continuous data and chi-square analysis of categorical data suggested optimal sample size for this study area is approximately 200 to 300 (0.05-0.1%).