This paper presents a systematic approach that integrates spline curve fitting and geometry analysis to extract full-waveform LiDAR features for land-cover classification. The cubic smoothing spline algorithm is used to fit the waveform curve of the received LiDAR signals. After that, the local peak locations of the waveform curve are detected using a second derivative method. According to the detected local peak locations, commonly used full-waveform features such as full width at half maximum (FWHM) and amplitude can then be obtained. In addition, the number of peaks, time difference between the first and last peaks, and the average amplitude are also considered as features of LiDAR waveforms with multiple returns. Based on the waveform geometry, dynamic time-warping (DTW) is applied to measure the waveform similarity. The sum of the absolute amplitude differences that remain after time-warping can be used as a similarity feature in a classification procedure. An airborne full-waveform LiDAR data set was used to test the performance of the developed feature extraction method for land-cover classification. Experimental results indicate that the developed spline curve- fitting algorithm and geometry analysis can extract helpful full-waveform LiDAR features to produce better land-cover classification than conventional LiDAR data and feature extraction methods. In particular, the multiple-return features and the dynamic time-warping index can improve the classification results significantly.