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A new detection model ('IMAG model') for estrus and mastitis in dairy cows was tested on four farms during several years. Such a test is necessary because information is lacking about the performance of detection models under field conditions. The test gave insight into the field performance of the IMAG model and the results were compared with the results of older models and with the results predicted by experts. Sensor data of milk yield, milk temperature, electrical conductivity of milk and animal activity were the inputs for the IMAG model. The IMAG model is based on time series analysis combined with a Kalman filter. This structure yields cow-dependent model parameters and combines data of different sensors. Results were compared with the manufacturer's model (supplied with the sensors), based only on exponential smoothing on data from one sensor. The sensor equipment differed between farms. The sensitivity (percentage of estruses detected) for estrus varied from 63 to 80%, depending on the threshold used. Specificity (non-estruses not detected as estrus) varied from 94 to 98%. The sensitivity for clinical mastitis varied from 55 to 80%, depending on the threshold used. The specificity for mastitis varied from 94 to 99%. Significant differences existed between farms, in sensitivity for estrus and mastitis. The applied equipment could only partially explain the differences in estrus and mastitis detection results between farms. No relation between stage of lactation and activity level was found, although a lower activity level in the first period of lactation might be expected. The main conclusion is that a detection model can give good results, but only if the equipment is working properly. The new model outperformed the manufacturer's model.