This paper documents an initial effort in detecting frequency-hopped (FH) signals in a multiple access (MA) environment from a machine learning perspective. Although, the offline training might require very intensive computing power, the extracted information does has a concise representation, which then enables to detect a signal using only simple and low-power operations. Frequency-hopping is an attractive alternative multiple access technique for direct sequence based code division multiple access (CDMA) schemes. Other than the communication channel statistic, the capacity of an FHMA system is determined by two major related design concerns: waveform design and receiver structure. Given the FH waveform, one still has difficulty in designing an FHMA ML receiver due to the facts that our knowledge about the channel statistics is often incomplete and even if it is complete the associated conditional probability density function (pdf) does not render a closed-form expression. Regarding the FHMA/MFSK waveform as a time-frequency pattern, we convert the multiuser detection problem into a pattern classification problem and then resolve to the Support Vector Machine (SVM) approach for solving the resulting multiple-class classification problem. By using an appropriate kernel function, the SVM essentially transforms the received signal space into a higher dimension feature space. We propose a SVM-based FHMA/MFSK receiver by applying the Sequential Minimization Optimization (SMO) and Directed Acyclic Graph (DAG) algorithms to find the optimal separating hyperplanes in the feature space. Simulation results indicate that our design does yield robust and satisfactory performance.