We present an approach for an autonomous system that detects a particular state of interest in a living cell and can govern cell fate accordingly. Cell states could be better identified by the expression pattern of several genes than of a single one. Therefore, autonomous identification can be achieved by a system that measures the expression of these several genes and integrates their activities into a single output. We have constructed a system that diagnoses a unique state in yeast, in which two independent pathways, methionine anabolism and galactose catabolism, are active. Our design is based on modifications of the yeast two-hybrid system. We show that cells could autonomously report on their state, identify the state of interest, and inhibit their growth accordingly. The system's sensitivity is adjustable to detect states with limited dynamic range of inputs. The system's output depends only on the activity of input pathways, not on their identity; hence it is straightforward to diagnose any pair of inputs. A simple model is presented that accounts for the data and provides predictive power. We propose that such systems could handle real-life states-of-interest such as identification of aberrant versus normal growth.