This paper presents the development of an intelligent temperature transducer to measure temperature in the range of 0 °C-100 °C using a negative temperature coefficient (NTC) thermistor. The NTC thermistor is connected in a timer circuit to convert the temperature change into frequency. The timer circuit acts as a signal conditioning circuit (SCC) for the NTC thermistor and exhibits a stable temperature-frequency characteristic with a reasonable error. The Levenberg-Marquardt training algorithm is used in a multilayer perceptron neural network to further reduce the nonlinearity error of the SCC. The trained artificial neural network (ANN) improved the linearity, sensitivity, and precision of the SCC to an appreciable range. A linearity of approximately ±0.8% and the sensitivity of about 5 kHz/°C are achieved. The intelligence of the trained ANN is embedded in a microcontroller unit, and the performance of the developed transducer is experimentally studied on a prototype board. © 2001-2012 IEEE.