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Ground water monitoring networks can provide vital information for sustainable water resources management. This involves the measurement of ground water level, solute concentration, or both. This article deals with the former. It optimizes network distribution of piezometer or data sampling wells to effectively monitor ground water levels under an irrigation region while retaining adequate overall measurement accuracy. This article presents a structured process for applying principal component analysis (PCA) in optimizing a ground water monitoring network in an irrigation area of Australia. The PCA functions, distributed with the MATLAB package, were used to determine relative contributions of individual piezometers in capturing the spatiotemporal variation of ground water levels. Kriging gridding interpolation algorithm was used to render the data surface presentations and determine spatial differences in piezometeric surfaces using different number of data sets. The results show that the overall difference of ground water level between the original piezometer network and the optimized networks after the PCA process was applied is less than 20%, while the total number of piezometers in the optimized network is reduced by 63%, which will save the time and cost to monitor ground water levels in the irrigation area. [ABSTRACT FROM AUTHOR]