An electric rotating machine can be defined as any form of apparatus which has a rotating member and generates, converts, transforms, or modifies electric power, such as a motor, generator, or synchronous generator. Although there are many variations, the two basic rotating machine types are synchronous and induction machines. The recent increasing use of rotating machines among other distributed generators is due to a number of advantages including peak shaving, improvement of the quality of power and reliability, power efficiency, environmental friendliness among others. Despite the above mentioned benefits of distributed power generation in the power grid, they have one major drawback, unintentional islanding. If this islanding condition is not detected in time or goes undetected, the distributed generator loses synchronism with the rest of the utility supply. This may lead to out of phase reconnection of the two systems and thus destroying the distributed generators and even lead to a total blackout in the power system. Again, upon the occurrence of an island, rotating machine based generators have another possible consequence of self-excitation. There is therefore need of fast detection of islanding condition especially when rotating machine based generators are integrated into the main power grid. There are many islanding detection methods and each has its merits and demerits. Their usage depends on certain factors including type of distributed generation in consideration and cost of implementation. Furthermore, the rotating machine based generators have the capability of sustaining an island. This makes the islanding detection and protection of these generators a bit challenging when compared with inverter based generators. This paper presents a passive islanding detection method, fuzzy logic algorithm, particularly on rotating machine based generators and its results analyzed under different conditions. After this analysis, it is concluded that the proposed method for islanding detection for rotating machine based generators is robust and accurate when implemented in the distribution network. This is because fuzzy logic control helps to improve the interpretability of knowledge-based classifiers through its semantics that provide insight in the classifier structure and decision-making process over crisp classifiers.