Overt forms of sexism have become less frequent (Swim Hyers, Cohen & Ferguson, 2001; Sue & Capodilupo, 2008). Nonetheless, scholars contend that sexism is still pervasive but often manifests as female microaggressions, which have been defined as often subtle, covert forms of gender discrimination (Capodilupo et al., 2010). Extant sexism scales fail to capture female microaggresions, limiting understanding of the correlates and consequences of women’s experiences of gender discrimination. Thus, the purpose of the current study was to develop the Female Microaggressions Scale (FeMS) based on an existing theoretical taxonomy and content analysis of social media data, which identifies diverse forms of sexism. Two separate studies were conducted for exploratory factor analysis (N = 582) and confirmatory factor analysis (N = 325). Exploratory factor analyses supported an eight-factor, correlated structure and confirmatory factor analyses supported a bifactor model, with eight specific factors and one general FeMS factor. Overall, reliability and validity of the FeMS (general FeMS and subscales) were mostly supported in the two present samples of diverse women. The FeMS’ subscales and body surveillance were significantly positively correlated. Results regarding correlations between the FeMS subscales and anxiety, depression, and life satisfaction were mixed. The FeMS (general FeMS) was significantly positively correlated with anxiety, body surveillance, and another measure of sexism but not depression or life satisfaction. Furthermore, the FeMS (general FeMS) explained variance in anxiety and body surveillance (but not depression, self-esteem, or life satisfaction) above and beyond that explained by an existing sexism measure and explained variance in anxiety and depression (but not self-esteem) above and beyond that explained by neuroticism. Implications for future research are discussed.