In this study we employed an Artificial Neuronal Network to analyze the forces flexing the monofin in reaction to water resistance. In addition we selected and characterized key kinematic parameters of leg and monofin movements that define how to use a monofin efficiently and economically to achieve maximum swimming speed. By collecting the data recorded by strain gauges placed throughout the monofin, we were able to demonstrate the distribution of forces flexing the monofin in a single movement cycle. Kinematic and dynamic data were synchronized and used as entry variable to build up a Multi-Layer Perception Network. The horizontal velocity of the swimmer’s center of body mass was used as an output variable. The network response graphs indicated the criteria for achieving maximum swimming speed. Our results pointed out the need to intensify the angular velocity of thigh extension and dorsal flexion of the feet, to strengthen velocity of attack of the tail and to accelerate the attack of the distal part of the fin. The other two parameters which should be taken into account are dynamics of tail flexion change in downbeat and dynamics of the change in angle of attack in upbeat.

• The one-dimensional structure of the monofin swimming creates favorable conditions to study the swimming technique.

• Monofin swimming modeling allows unequivocal interpretation of the propulsion structure. This further permits to define the mechanisms, which determine efficient propulsion.

• This study is the very first one in which the Neuronal Networks was applied to construct a functional/applicable to practice model of monofin swimming.

• The objective suggestions lead to formulating the criteria of monofin swimming technique, which plays the crucial role in achieving maximal swimming speed.

• Theoretical and empirical (realistic) verification created by parameters indicate by neural networks, paves the way for creating suitable models, which could be employed for other sports.