Agricultural crop management decisions often require data on hydraulic properties of soils. Little information is available on hydraulic properties of clay soils that are impounded by rainwater (known as ‘Haveli’ lands) every year during the monsoon season in large tracts of Madhya Pradesh in India. Estimating hydraulic properties using global pedotransfer functions (PTFs) is one possible way to collect such information. Rules in the widely used global PTF Rosetta were executed to obtain estimates of two important hydraulic properties, namely soil water retention characteristics (SWRC) and saturated hydraulic conductivity (Ks). SWRC estimates obtained with maximum input (particle size distribution, bulk density, field capacity and permanent wilting point) in Rosetta were relatively closer to the laboratory-measured data as compared with the estimates obtained with lower levels of input. Root mean square error (RMSE) of estimates ranged from 0·01 to 0·05 m3/m3. Hierarchical PTFs to predict Ks from basic soil properties were derived using statistical regression and artificial neural networks. Evaluation of these indicated that neural PTFs were acceptable and hence could be used without loss of accuracy.