We interpret real-valued black-box optimization problems over continuous domains as black-box landscapes. The performance of a given optimization heuristic on a given problem largely depends on the characteristics of the corresponding landscape. Designing statistical measures that can be used to classify landscapes and quantify their topographical properties is hence of great importance. We transfer the concept of fitness-distance analysis from theoretical biology and discrete combinatorial optimization to continuous optimization and assess its potential to characterize black-box landscapes. Using the CEC 2005 benchmark functions, we empirically test the robustness and accuracy of the resulting landscape characterization and illustrate the limitations of fitness-distance analysis. This provides a first step toward a classification of real-valued black-box landscapes over continuous domains.