Vertical profiles of wind velocity and air temperature from a sound detection and ranging (sodar) radio acoustic sounding system (RASS)-derived dataset within an alpine valley of the New Zealand Southern Alps were analyzed. The data covered the month of September 2013, and self-organizing maps (SOM; a dataclustering approach that is based on an unsupervised machine-learning algorithm) are used to detect topological relationships between profiles. The results of the SOM were shown to reflect the physical processes within the valley boundary layer by preserving valley boundary layer dynamics and its response to wind shear. By examining the temporal evolution of ridgetop wind speed and direction and SOM node transitions, the sensitivity of the valley boundary layer to ridgetop weather conditions was highlighted. The approach of using a composite variable (wind speed and potential temperature) with SOM was successful in revealing the coupling of dynamics and atmospheric stability. The results reveal the capabilities of SOM in analyzing large datasets of atmospheric boundary layer measurements and elucidating the connectivity of ridgetop wind speeds and valley boundary layers.