Scientists and investigators in such diverse fields as geological and environmental sciences, ecology, forestry, disease mapping, and 
economics often encounter spatially referenced data collected over a fixed set of locations with coordinates (latitude-longitude, 
Easting-Northing etc.) in a region of study. Such point-referenced or geostatistical data are often best analyzed with Bayesian hierarchical 
models. Unfortunately, fitting such models involves computationally intensive Markov chain Monte Carlo methods whose efficiency depends 
upon the specific problem at hand. This requires extensive coding on the part of the user and the situation is not helped by the lack of 
available software for such algorithms. Here, we introduce a statistical software package, spBayes, built upon the R statistical computing 
platform that implements a generalized template encompassing a wide variety of Gaussian spatial process models for univariate as well as 
multivariate point--referenced data. We discuss the algorithms behind our package and illustrate its use with a synthetic and real data example.