Accurate identification of failures in photovoltaic (PV) systems, which can result in energy loss or even serious safety issues, is crucial for ensuring reliability of the installations and the guaranteed lifetime output. The scope of this paper is to present the development of failure detection routines (FDRs) that operate on acquired data-sets of grid-connected PV systems in order to diagnose the occurrence of failures. The developed FDRs comprise of a failure detection and classification stage. Specifically, the failure detection stage was based on a comparative statistical approach between the measured and simulated electrical measurements. In parallel, fuzzy logic inference was performed in order to analyse the failure pattern and classify accurately the occurred fault. The fuzzy rule based classification system (FRBCS) models were constructed for each failure through a supervisory learning process, trained with continuous samples split in a 70:30 % train and test set approach of acquired data-sets which included the feature patterns exhibited during normal and faulty operation. The results obtained by emulating three failure patterns (partial shading, inverter shutdown and bypass diode failure), showed that the developed FDRs were capable of detecting accurately the faults upon their occurrence by signifying detectable discrepancies through the daily statistical comparisons of the measured and simulated electrical parameters. Finally, the developed classification models showed high accuracy of classifying each failure occurrence within the test set used for benchmarking. Specifically, the success rate obtained with the FRBCS models was 100 %, 96.9 % and 96.53 %, when classifying the inverter shutdown failure, bypass diode fault and partial shading, respectively.  ON-LINE FAILURE DIAGNOSIS OF GRID-CONNECTED PHOTOVOLTAIC SYSTEMS BASED ON FUZZY LOGIC