Algorithms for finding fundamental frequency based on ACF result
im assuming your signals are audio and harmonic and you are not doing multiple F0 estimation where you have multiple tones played simultaneously. in that case there are a few easier methods. the normalisation of short time autocorrelation function. r(lag) = (1/N)*sum(x(n)*x(n+lag)
N is length of x(n)
F0 here is obtained from the inverse of the lag that corresponds to the maximum of r(lag) within a predefined range (for example your range could be from 20 to 250, hence at lag 196 you have a maximum, again play around with this range). to avoid detecting an integer multiple of the period, short lags are better than long ones.
here is a C function for this, probably worth playing around with. 

void autocorr(  
    float *r,       /* (output) autocorrelation vector */ 
    const float *x, /* (input) data vector */ 
    int N,          /* length of data vector */ 
    int order       /* largest lag for calculated  
                      autocorrelations */ 
)
{ 
    int     lag, n;
    float   sum; 

    for (lag = 0; lag &lt;= order; lag++) { 
        sum = 0; 
        for (n = 0; n &lt; N - lag; n++) { 
            sum += x[n] * x[n+lag]; 
        } 
        r[lag] = sum/N; 
    } 
} 


An alternative method for detecting peaks, is center clipping. after your autocorr function you could code something like:

y(n) = a(n) - C,  if a(n) >= C

y(n) = a(n) + C, if a(n) &lt;= -C

y(n) = 0 otherwise.

C is some percentage of the max amplitude of the autocorr vector.

all i have showed you above is the very basic stuff, since F0 analysis can be very complex (probably why people have not answered). you can find more in speech codec books or music transcription books.