In-line sensors were used to measure quarter milk conductivity and milk temperature in the milking claw for monitoring mastitis in dairy cows. In a preliminary experiment, sensor data were used to develop algorithms and threshold values for the detection of mastitis. In a later experiment, these thresholds were implemented in a monitoring system. Thresholds were determined in order to detect severe (clinical) mastitis in an early stage and subclinical mastitis, respectively. Daily milk yield and temperature served to establish the severity of mastitis. Nearly all cases of clinical and 50% of the cases of subclinical mastitis were detected. From results of measurements it appeared that onset and cessation of oestrus-related mounting activity could be predicted by pedometers. This information was used to correlate oestrus-insemination interval and conception rate. The probability of conception was highest between 6 and 17 h after increased pedometer activity with an estimated optimum at 12 h. To improve the reliability a detection model has been developed to process the measured variables in a combined way. It can be concluded that the detection model improves the sensitivity of oestrus detection by 10-20%. The sensitivity of detection of clinical and subclinical mastitis appeared to be 90% and 76%, respectively. However, the demands concerning the specificity (defined as: percentage of truly negatives indicated correctly) may not be met.