Inappropriate patient-ventilator interactions' (PVI) quality is associated with adverse clinical consequences, such as patient anxiety/fear and increased need of sedative and paralytic agents. Thus, technological devices/tools to support the recognition and monitoring of different PVI quality are of great interest. In the present study, we investigate two tools based on a recent landmark study which applied recurrence plots (RPs) and recurrence quantification analysis (RQA) techniques in non-invasive mechanical ventilation. Our interest is in how this approach could be a daily part of critical care professionals' routine (which are not familiar with dynamical systems theory methods and concepts). Two representative time series of three typical PVI "scenarios" were selected from critically ill patients subjected to invasive mechanical ventilation. First, both the (i) main signatures in RPs and the (ii) respective signals that provide the most (visually) discriminant RPs were identified. This allows one to propose a visual identification protocol for PVIs' quality through the RPs' overall aspect. Support for the effectiveness of this visual based assessment tool is given by a RQA-based assessment tool. A statistical analysis shows that both the recurrence rate and the Shannon entropy are able to identify the selected PVI scenarios. It is then expected that the development of an objective method can reliably identify PVI quality, where the results corroborate the potential of RPs/RQA in the field of respiratory pattern analysis.