A situation between several moving aircraft is a conflict when their position is less than the internationally specified distance. To solve aircraft conflicts, air traffic controllers consider many parameters including the positioning coordinate, speed, direction, weather, etc. of the involved aircraft. This is a complex task, specifically considering the increase of the traffic. Assisting systems could help controllers in their tasks. Most conflict resolution models are based on trajectory data of a fixed number of input aircraft. Under this constraint, it is possible to resolve conflicts using machine learning models, including convolutional neuron network models. Such models cannot resolve conflicts that imply a variable number of aircraft because the input size of the model is fixed. To solve this challenge, we transformed the trajectory data into images which size does not depend on the number of planes. We developed a multi-label conflict resolution model that we named ACRnet, based on a convolutional neural network to classify the obtained images. ACRnet model achieves an accuracy of 99.16% on the training data and of 98.97% on the test data set for two aircraft. For both two and three aircraft, the accuracy is 99.05% (resp. 98.96%) on the training (resp. test) data set.