Mastitis is one of the most prevalent and costly diseases in dairy operations. Key components for adequate mastitis control are the detection of early stages of infection, as well as the selection of therapy based on the causal pathogen associated with infection. Our objective was to characterize the pattern of electrical conductivity (EC), provided by an in-line mastitis detection system, considering specific mastitis-causing pathogen group involvement. Cows (n = 200) identified by the system with a deviation >15% in the manufacturer’s (Afimilk Ltd., Kibbutz Afikim, Israel) proprietary algorithm for EC (HEC) were considered cases and enrolled in the study. One control (CON), defined as within normal ranges for EC, was matched to each case and monitored for milk yield (MY) and EC for ± 10 d. A sterile pooled milk sample was collected from each cow for bacteriological culture. Pathogens were categorized into gram-positive (GP), gram-negative (GN), other (OTH), and no growth (NO). Data were submitted for repeated measures analysis (PROC MIXED, SAS), with EC as dependent variable. EC status (HEC or CON), bacterial categories, and milking relative to d of enrolment were considered independent fixed variables and farm was included as a random effect in the model. For HEC animals, EC was greater in NO than in GN (P = 0.036) but EC was not different among other pathogen groups. For CON animals, EC was greatest in OTH compared with NO (P = 0.03), GP (P = 0.03), and GN (P = 0.07). However, EC was not different when comparing between GP and GN. For HEC animals, MY was not significantly different among pathogen groups. For CON, GN had greater MY than NO (P = 0.006) and GP (P = 0.02). For HEC and CON animals, EC was greater in multiparous cows than in primiparous cows (P < 0.001). State of lactation had no effect in CON animals whereas, for HEC cows, EC was greater in animals in early (P < 0.0001) and late lactation (P = 0.0015), compared with mid lactation cows. Thus, it is concluded that EC variation cannot solely be attributed to pathogen groups and multiple factors should be considered in developing mastitis pathogen detection models based on EC.