Online temperature monitoring of electric motors is essential to the safety of the system under dynamic operation. The number of temperature sensors and their locations are often limited due to physical constraints and cost of the hardware, and the temperatures for most parts of a motor cannot be directly measured. To estimate the instantaneous temperature distribution of a motor, we design an observer for the real-time temperature monitoring using a thermal circuit model and limited measurements. Challenges imposed to the observer design include unknown heat sources and measurement noises. The observer needs to be able to simultaneously estimate all the hidden states and unknown inputs, while dealing with measurement noises. In this paper, different observers, including Kalman filter, Luenberger observer, adaptive observer, and modified proportional-derivative (PD) observer are designed to address the problem. We first give some background information of the problem, and introduce the thermal circuit model; then describe the observer designs with a focus on PD observer, which is more recently developed. The proposed observers are then implemented with simulations, and their performances are evaluated and compared.