This paper introduces a Bayesian inversion approach to estimating steady state ocean circulation and tracer fields. It is based on a quasi-horizontal flow model and a PDE solver for the forward problem of computing solutions to the tracer field advection-diffusion equations. A typical feature of existing ocean circulation inverse methods is a preprocessing stage in which the tracer data are interpolated over a regular grid and the interpolation error is ignored in the subsequent inversion. Our approach only uses interpolated data at those grid points that have neighboring hydrographic stations. By exploiting physically-based models in an integrated fashion, the method provides a statistically unified inversion and tracer field reconstruction with minimal data smoothing. Solving the problem consists of finding information about the circulation and tracer fields in the presence of a number of assumptions (prior information); the resulting posterior probability distribution summarizes what we can know about these fields. We develop a Markov chain Monte Carlo simulation procedure to extract information from the (analytically intractable) posterior distribution of all the parameters in the model; uncertainty about the “solution” is represented by variation in the output of the simulation runs. Our approach is aimed at finding the time-averaged quasi-horizontal flow and tracer fields for an abyssal neutral density layer in the South Atlantic. The collection of oceanographic data during the past century has formed the basis for our understanding of the distribution of properties and the circulation in the ocean. However, a description of the steady flow and associated property fields in the oceanic interior that is dynamically consistent with those collective observations and that accounts for the inherent uncertainty associated with measurements, physical variability and model parameterizations, for example, is still lacking. It may be that the steady-state assumption is a poor approximation to the equations of motion, but this hypothesis deserves close attention as a test of our understanding of the basic physics of the ocean circulation. In this paper we introduce a physically-based statistical inversion approach to estimate the