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Energy efficiency has become one of the main concerns as a result of growing energy demands. Since electric machines are one of the major energy consumers, their efficiency is critical. The losses in electric machines consist of copper loss, mechanical loss, and core loss. According to previous studies, core losses constitute 20%-25% of the total losses in sinusoidal voltage fed machines. These losses further increase when the machine is fed with pulse width modulated variable speed drives. The complex nature of core losses and the measurement limitations complicate the core loss analysis in electric machines due to the non-uniformity of flux densities. Moreover, some additional effects such as minor hysteresis loops, high-frequency slot harmonics, and DC bias further increase the complexity of the analysis. Currently, core loss estimations are performed in finite element analysis (FEA) software packages using flux density waveforms in each element of the model. These methods are based on Steinmetz equation and the loss separation principle, with the coefficients of the respective nonlinear equations determined using the core loss measurement data under certain conditions. When it comes to the electric machine core loss estimation, these models result in more than 100% estimation error in some cases, even at rated conditions. Although there are mathematical hysteresis models that can accurately estimate the total core losses, these models are very complex, they require detailed material information, and they are hard to integrate into the FEA software. Therefore, a better method that can accurately estimate the core losses in electric machines with a less complicated procedure is highly desirable. In this dissertation, core loss measurements and estimations based on the actual flux density waveforms of the electric machines are investigated. An approach has been developed to estimate the core losses under any operating condition. The study is organized as three related tasks. The first one is to develop a test system that can generate the desired flux density waveforms in a core under test (CUT) with/without a DC offset. This task includes hardware and control algorithm development sub-tasks. The second task is to obtain the flux density waveforms in the regions that have a uniform loss distribution in the machine, and then, estimate the total core loss by generating the flux density waveforms in a toroidal core made out of the same material as is used in the machine. The final task is to measure the core losses at the load conditions. After completing all three tasks, the results show that the core loss estimation technique developed in this dissertation decreases the estimation error to less than 10%.