Sports biomechanics is the science that applies the laws of mechanics and physics to athlete performance, in order to gain a greater understanding of motor skills through measurement, modeling and simulation. It operates for meeting the growing demands of coaches, physicians and athletes, to quantitatively assess the essential characteristics of performance. In clinical �gait analysis�, standard protocols have been widely validated and quantitative analysis has become a powerful tool for surgical decision and for post-operative and rehabilitative monitoring. In sports field the great amount of disciplines and the difficulty in standardizing movements have acted as a brake on the systematic use of powerful techniques like stereophotogrammetry. Therefore, one of the purposes of sports research should be the identification of the peculiar characteristics and of the most proficient strategy for each athlete. The monitoring of a group of athletes should be the base for an accurate quantitative assessment of the movement under analysis and for the identification of different skill levels. Then, the knowledge of single athlete�s abilities or deficiencies should help coaches to adjust individual training programs. Moreover, a reliable characterization of the subject should pass through longitudinal monitoring: the athlete might be compared with himself in different times during the training season. The purpose of this study was to investigate the use of principal component analysis for reducing and interpreting sports motion data, while accounting for their original variability. Race walking was chosen as the mean of investigation, because it is a motor task that presents peculiar biomechanical and coordinative demands. An optoelectronic system and a force plate were used to collect and estimate kinematics and kinetics of seven race walkers of international level. Several race walking repetitions were acquired and kinematics and kinetics variables were processed. Principal component analysis summarized the most important information in the data, by representing the variables in a limited number of components that explained most of data variance. Data underwent three different applications of PCA: traditional (t-PCA), functional (f-PCA) and two-stage (2-PCA). A further objective of this study was to evaluate the advantages and disadvantages of the three methods in solving different challenges, because these techniques have not been widely adopted in sports research. A general characterization of race walking biomechanics was pursued, in order to get a full comprehension of the movement under analysis. Then, a robust and complete characterization of the single athlete�s performance strategy was given. All the three methods allowed the exploitation of the relationships among multiple measures in the analysis of race walking data. The most important factors that distinguish athletes according to their skill levels were found out. Moreover, the peculiar technical and coordinative characteristics of each athlete were widely described. Finally, an example of longitudinal monitoring was described. Motion analysis, combined with PCA, was used on data from two subsequent testing sessions, to identify the main improvements caused by training.