A fuzzy logic hydrometeor classification algorithm (HCA), allowing discrimination between six microphysical species regardless of the radar wavelength is presented and evaluated. The proposed method is based upon combination sets of dual-polarimetric observables (reflectivity at horizontal polarization ZH, differential reflectivity ZDR, specific differential phase KDP, correlation coefficient ρHV) along with temperature data inferred from a numerical weather prediction model output.The performance of the HCA is evaluated using 20 h of multi-frequency dual-polarimetric radar data collected during the first Special Observation Period (SOP1) of the Hydrological Cycle in the Mediterranean Experiment (HyMeX). A new method based upon intercomparisons of retrieved hydrometeor data deduced from pairs of neighbouring radars (S-band vs. S-band and S-band vs. C-band) over a common sampling area is proposed to evaluate the consistency of hydrometor classification outputs. S-/C-band radar comparisons generally show better consistency than S-/S-band radar comparisons due to issues with the identification of the 0°C isotherm on one of the two S-band radars. Imperfect attenuation correction at C-band may also lead into differences in hydrometeor fields retrieved from the C- and S-band radars in convective situations, but retrieved hydrometeor data are globally very consistent from one radar to another. Comparisons against in situ airborne data also confirm the overall good performance of the HCA.In a second experiment, an original method allowing the production of multi-radar three-dimensional (3D) hydrometeor fields from single-radar 2D hydrometeor data is tested on a bow-echo convective system observed with C- and S-band radars. The resulting 3D hydrometeor fields provide a detailed view of the bow-echo microphysical structure and confirm the good performance of both the HCA and interpolation technique.