We present interactive techniques for identifying and extracting features in function fields. Function fields map points in $n$-dimensional Euclidean space to 1-dimensional scalar functions. Visual feature identification is accomplished by interactively rendering scalar distance fields, constructed by applying a function-space distance metric over the function field. Combining visual exploration with feature extraction queries, formulated as a set of function-space constraints, facilitates quantitative analysis and annotation. Numerous application domains give rise to function fields. We present results for two-dimensional hyperspectral images, and a simulated time-varying, three-dimensional air quality dataset.