Irrigation management practices contribute to the production of grapes, improving quality for winemaking and the sustainability of the vineyards as well. Precision agriculture based on spatial variability information allows the identification of zones with different demand on irrigation in an orchard, improving water use efficiency (saving water and energy). Reducing the amount of information used to define management zones can allow the reduction of the costs of precision farming practices and, consequently, making the technique more acceptable to producers. The objective of this study was to delimit irrigation management zones through the application of geostatistical and multivariate analysis of soil texture data in a 1.1 ha wine vine orchard (cv. Chardonnay / Paulsen 1103) located at Espirito Santo do Pinhal, State of São Paulo, Brazil. Soil samples were collected in 54 georeferenced points in 0-0.20 m and 0.20-0.40 m layer depths to determine the soil bulk density (undisturbed samples; steel cylinders, 5cm high and 5cm diameter) and the particle size distribution (PSD) using an automated gamma-ray attenuation equipment. Soil water retention curves (SWRC) were then estimated from the PSD data using the Ayra-Paris method. Soil water (AW) and readily available soil water (RAW) were determined from the SWRC data. Soil texture (amounts of sand, silt and clay), AW and RAW data were submitted to descriptive statistics, the Kolmogorov–Smirnov test and Pearson's correlation analysis. Soil texture data were submitted to geostatistical analysis to characterize the spatial distribution and their interpolation. Multivariate analysis (principal component analysis and fuzzy c-means cluster analysis) was applied to determinate the management zones, and best number (between 2 and 6 zones) was tested based on two statistical indexes. The analysis of variance and Tukey test were performed using soil texture, AW, and RAW for validation of the management zones. The best management zones number was four in both soil layers, and all zones showed significant differences for at least one of the three granulometric fractions (sand, silt and clay). Lower amounts of silt + clay were related with the reduction of AW and RAW, allowing to select the irrigation management zones based only on the soil texture data in this area. Results of this work helped to select the best points for installation of soil moisture probes and the implementation of independent irrigation plots.