Soil structure is known to have significant impact on soil hydraulic properties, but is rarely represented directly in soil hydraulic PTFs. Most PTFs use input parameters that are indirectly related to soil structure. However, morphological descriptions of soil structure are often routinely collected during field sampling and represent an underutilized resource. No clear recommendation exists on what structural indicators might be the most significant in relation to the estimation of soil hydraulic properties. We used regression trees to examine, which types of soil texture- and structure-related data provide the most useful information towards estimating soil hydraulic properties. We coupled the regression tree technique with ‘bagging’; meaning that alternative realizations of the input data set were generated. Two hundred realizations have been generated from a European data set (N=502) using randomized subset selection. In each case, samples not selected for tree development were used to test the performance of the tree models. Input variables were: the representation of any of 7 ped-size classes; the orientation of any structural cracks; classification of apedal soils; top/subsoil distinction; USDA texture classes; sand, silt and clay content, bulk density, and organic matter content. The probability of appearance of input attributes at the branch splits, and their split values were evaluated. We developed additional tree models after systematically eliminating groups of the above input information. Routinely collected morphological data were efficient in estimating saturated hydraulic conductivity, and could eventually be used to replace lab measured input data.