The Surface Classifier approach for the fusion of sensor data has been shown to produce improved classification performance over traditional methods that characterize object classes as a single mean feature vector and associated covariance. The key aspect of this approach is the notion of characterizing object classes as parametric representations of curves, or surfaces, in feature space that capture the underlying correlations between features. By performing calculations in this representation of feature space, the fusion of feature data from the two sensors was seen to be straightforward. In this paper, the Surface Classifier approach is extended to combine multiple observations of these objects into a 'manifold fragment' that is fitted to the surface representing an object's parametric representation in feature space. Additionally, by using a 'Torn-Surface' representation of the object classes, the approach is able to address discontinuities in object class representations and give estimates of non-observed, derived object features (e.g., physical dimensions). As will be shown, with added white noise, classification errors and the errors in estimating the derived features increase but remain very well behaved.