The application of numeric methods to the minimization of error has become an emerging paradigm for obj ect recovery. Typically, a parametric representation describing the object is postulated. Its parameters are then adjusted to minimize some measurement of the distance between the representation and the datapoints (the error-of-fit model). Characteristics of the sensor used to recover the points may be implicit in this formulation or may not be included at all. While sensors may be precise for a specific field of view no sensor is everywhere exact. A laser range finder for example, yields very sharp x- and y-coordinate values; however, its z-coordinate is less trustworthy. It becomes important to capture the strengths and weaknesses of a sensor and incorporate them into the recovery process. We seek to make explicit the contribution of a particular sensor by introducing a sensor model. This partitioning facilitates the development of an appropriate description of a sensor's characteristics. Also, it helps clarify interactions among different aspects of the recovery process ( i.e. error-of-fit model, sensor model, and parametric object representation). The sensor model is reflected in the certainty of sensed quantities (position, color, intensity) associated with a datapoint. We explore whether the introduction of an explicit sensor model yields an improvement in the recovery process. The PROVER (Parametric Representation Of Volumes: Experimental Recovery) System, a testbed used in the development of sensor models is described.