In radiotherapy, organ motion mitigation by means of dynamic tumor tracking requires continuous information about the internal tumor position, which can be estimated relying on external/internal correlation models as a function of external surface surrogates. In this work, we propose a validation of a time-independent artificial neural networks-based tumor tracking method in the presence of changes in the breathing pattern, evaluating the performance on two datasets. First, simulated breathing motion traces were specifically generated to include gradually increasing respiratory irregularities. Then, seven publically available human liver motion traces were analyzed for the assessment of tracking accuracy, whose sensitivity with respect to the structural parameters of the model was also investigated. Results on simulated data showed that the proposed method was not affected by hysteretic target trajectories and it was able to cope with different respiratory irregularities, such as baseline drift and internal/external phase shift. The analysis of the liver motion traces reported an average RMS error equal to 1.10 mm, with five out of seven cases below 1 mm. In conclusion, this validation study proved that the proposed method is able to deal with respiratory irregularities both in controlled and real conditions.