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Abstract: Robotic needle insertion based on haptic feedback can be imprecise and error\-prone, especially for sudden force changes in case of ruptures. To predict rupture events early during tissue deformation, knowledge is required about the type and characteristics of the tissues involved. Several approaches to this exist and increase system complexity by including additional sensors or imaging modalities. We introduce a new approach based on formal model checking, which allows us to identify tissue by a directed search through the state space of a needle insertion model. Using force data measured at the needle shaft during cutting motion, our method identifies the most probable tissue iteratively at run\-time, based on a priori information of possible tissues. In a case study of needle insertions into gelatin phantoms with varying gelatin\-water ratios, our approach allowed 90.7% correct identifications and may thus be considered to identify tissue during robotic needle insertion