Fault diagnostics in industrial applications has become very popular research topic. In agriculture the time window to complete tasks may be very short. Timing the field operations is critical when machine and human resources are limited. Different kind of dynamic model based fault detection and diagnosis methods have been studied throughout the process automation context. Here fault diagnostics has been studied with three agricultural implements, two drills and one sprayer In the first drill, scalar statistical analysis and classification of temporal feature patterns can be applied to analysis of possible faults in the analog measurements. In the second drill fault diagnostics was developed for electrical actuators. The nominal model of actuator was identified with test procedure, and the faults are detected by comparing the measured behavior to the nominal model In the sprayer the pump is the most critical component with many different failure modes. Frequency domain analysis and fuzzy classifier were utilized to detect faults. Different faults were induced to the pump and the diagnostics were developed based on the collected data. Remote diagnostics is needed if the failures are hard to detect automatically. For example some actuators change their behavior slowly due to wearing and it may be difficult to know when the fatal failure will appear. By implementing remote diagnostics the manufacturer can centrally collect fault diagnostics data and use statistical methods for improving and fine tuning preventive maintenance programs. Here the remote diagnostics system was developed to one implement, hydraulic actuator condition monitoring in one drill. The remote diagnostics features were implemented in the Task Controller of an ISOBUS compatible machine control system. In all cases the self diagnostics methods were implemented as software components to the implement controller software and were tested for functionality with real agricultural implements.