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Condition monitoring and machinery fault diagnosis are central to the implementation of efficient maintenance management strategies. They can be based on empirical modelling, which aims at associating measured data to machine conditions. Arguably, different monitoring tasks present different challenges to the maintenance engineer. This paper presents the development of a flexible software solution for condition monitoring, novelty identification and machinery diagnostics, which can easily be customised to a wide range of monitoring scenarios. Its main constituents are a number of independent software modules, such as the fault and symptom tree, the fuzzy classification module, the novelty detection and the neural network diagnostics sub-systems. It is implemented on two different applications, namely machine tool monitoring and gearbox monitoring.