Point-of-care sensors are used in clinical applications for diagnosing and monitoring health conditions. For example, a point-of-care sensor for therapeutic drug monitoring of the clozapine antipsychotic can reduce burdens from guidelines suggesting routine monitoring of this medication. However, when measuring chemical markers in complex fluids, there are challenges related to decreased sensor performance due to chemical interference. This work presents a methodology for identifying individual interfering species. A set of cross-reactive electrochemical sensors were developed, whose diversified responses provide a fingerprint-type pattern capable of differentiating various species. By mapping the multidimensional responses, patterns from complex solutions were discerned and matched to those of individual species. Applying this methodology to clozapine sensing in blood, a major source of chemical interference was identified. The understanding matrix components that cause interference can guide the design of reliable sensing systems and can be integrated with pattern recognition tools that can account for it.